Search

Russ's (Belk, 1988) extension of William James' reflections on the self has been a significant accelerant of interpretive and Consumer Culture Theoretic research. In this paper, I will outline a different way we can engage with James' oeuvre; specifically via psychical research and parapsychology. These fields are subsequently linked to recent debates on quantum interconnectedness, nonlocality, brain-computer interfaces and capitalist-materialist telepathy. This endeavour initially responds to Russ's point that 'What we call "self" is really "selves". Besides distributed selves and multiple selves shared between individuals, there are also multiple selves within individuals' (Belk, 2024, p. 571). It builds upon Craig's respective accounts by proffering materialist and post-materialist perspectives on human-human dynamics and human-computer technological affordances. As we shall see, William James encourages us to recognise multiple selves, multiple levels of consciousness, and the psychical extension of self. [ABSTRACT FROM AUTHOR]

Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.

BACKGROUND: In the United States, there are over seven million stroke survivors, with many facing gait impairments due to foot drop. This restricts their community ambulation and hinders functional independence, leading to several long-term health complications. Despite the best available physical therapy, gait function is incompletely recovered, and this occurs mainly during the acute phase post-stroke. Therapeutic options are limited currently. Novel therapies based on neurobiological principles have the potential to lead to long-term functional improvements. The Brain-Computer Interface (BCI) controlled Functional Electrical Stimulation (FES) system is one such strategy. It is based on Hebbian principles and has shown promise in early feasibility studies. The current study describes the BCI-FES clinical trial, which examines the safety and efficacy of this system, compared to conventional physical therapy (PT), to improve gait velocity for those with chronic gait impairment post-stroke. The trial also aims to find other secondary factors that may impact or accompany these improvements and establish the potential of Hebbian-based rehabilitation therapies. METHODS: This Phase II clinical trial is a two-arm, randomized, controlled, longitudinal study with 66 stroke participants in the chronic (> 6 months) stage of gait impairment. The participants undergo either BCI-FES paired with PT or dose-matched PT sessions (three times weekly for four weeks). The primary outcome is gait velocity (10-meter walk test), and secondary outcomes include gait endurance, range of motion, strength, sensation, quality of life, and neurophysiological biomarkers. These measures are acquired longitudinally. DISCUSSION: BCI-FES holds promise for gait velocity improvements in stroke patients. This clinical trial will evaluate the safety and efficacy of BCI-FES therapy when compared to dose-matched conventional therapy. The success of this trial will inform the potential utility of a Phase III efficacy trial. TRIAL REGISTRATION: The trial was registered as BCI-FES Therapy for Stroke Rehabilitation on February 19, 2020, at clinicaltrials.gov with the identifier NCT04279067.

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating avatars (herein defined as an extension of digital twins) of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain–computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery–based brain–computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion–extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery–related event-related desynchronization and improved task classification performance. [ABSTRACT FROM AUTHOR]

Glutamate is one of the most important excitatory neurotransmitters within the mammalian central nervous system. The role of glutamate in regulating neural network signaling transmission through both synaptic and extra‐synaptic paths highlights the importance of the real‐time and continuous monitoring of its concentration and dynamics in living organisms. Progresses in multidisciplinary research have promoted the development of electrochemical glutamate sensors through the co‐design of materials, interfaces, electronic devices, and integrated systems. This review summarizes recent works reporting various electrochemical sensor designs and their applicability as miniaturized neural probes to in vivo sensing within biological environments. We start with an overview of the role and physiological significance of glutamate, the metabolic routes, and its presence in various bodily fluids. Next, we discuss the design principles, commonly employed validation models/protocols, and successful demonstrations of multifunctional, compact, and bio‐integrated devices in animal models. The final section provides an outlook on the development of the next generation glutamate sensors for neuroscience and neuroengineering, with the aim of offering practical guidance for future research. [ABSTRACT FROM AUTHOR]

The text generation technique employs the transformation of the word document from the source to the targeted document based on the sequence to sequence generation. Video captioning, language identification, image captioning, recognition of speech, machine translation, and several other natural language generations are the application areas of the text generation techniques. The Electroencephalographic (EEG) signals record brain activity and are considered the source of information for using the brain-computer interface. Several kinds of research were developed for text generation. The most challenging task is more accurate text generation by considering the large contextual information and the significant features for generating the text. Hence, in this research, text generation using Folded deep learning is proposed for generating the text through text prediction and suggestion through the non-invasive technique. The EEG signal recorded from the patients is utilized for the prediction of the first letter using the proposed Folded Ensemble Deep convolutional neural network (DeepCNN), in which the hybrid ensemble activation function along with the folded concept in validating the training data to obtain the network stability and to solve the class imbalance issue. Then, the next letter suggestion is employed using the proposed Folded Ensemble Bidirectional long short-term memory (BiLSTM) approach based on the eye-blink criteria for generating the sequence-to-sequence text generation. The enhanced performance is evaluated using accuracy, precision, and recall and acquired the maximal values of 97.22%, 98.00%, and 98.00%, respectively. The proposed method can be utilized for real-time processing applications due to its non-invasive nature. [ABSTRACT FROM AUTHOR]

AbstractOpen hardware and the need for ecologically valid measurements drive the Electroencephalography (EEG) democratization movement—EEG has been steadily transcending the boundaries of clinical research, making its way into interdisciplinary fields. In Human-Computer Interaction (HCI), EEG is used to measure cognitive workload and infer cognitive processes for building cognition-aware systems. We describe and evaluate our BCIglass prototype where EEG electrodes are embedded in the frame of a mainstream Head-Mounted Display (HMD) to create a skull-peripheral topology. We devised a lab study with 34 participants who completed seven established cognitive tasks. Then, we conducted a pilot field study with one participant to test BCIglass in everyday-life settings. Our findings demonstrate that BCIglass captures EEG activity in a manner comparable to a research-grade EEG-cap system. Our topology infers the cognitive task at hand, and the underlying cognitive process(es) by proxy, with an accuracy of ∼80% and only three electrodes at the skull periphery. Embedding EEG electrodes in lightweight HMDs represents a promising approach in the quest to achieve ubiquitous brain-computer interfacing in real-world settings. [ABSTRACT FROM AUTHOR]

The article discusses recent advances in brain-computer interfaces (BCIs) designed to restore speech for individuals with severe speech impairments. Topics include the development of BCIs for patients with severe dysarthria and anarthria, the technology behind these interfaces that allows direct communication between the brain and external devices, and the progress in understanding neural dynamics and artificial intelligence that enables effective speech restoration.

The durability of communication with the use of brain-computer interfaces in persons with progressive neurodegenerative disease has not been extensively examined. We report on 7 years of independent at-home use of an implanted brain-computer interface for communication by a person with advanced amyotrophic lateral sclerosis (ALS), the inception of which was reported in 2016. The frequency of at-home use increased over time to compensate for gradual loss of control of an eye-gaze-tracking device, followed by a progressive decrease in use starting 6 years after implantation. At-home use ended when control of the brain-computer interface became unreliable. No signs of technical malfunction were found. Instead, the amplitude of neural signals declined, and computed tomographic imaging revealed progressive atrophy, which suggested that ALS-related neurodegeneration ultimately rendered the brain-computer interface ineffective after years of successful use, although alternative explanations are plausible. (Funded by the National Institute on Deafness and Other Communication Disorders and others; ClinicalTrials.gov number, NCT02224469). [ABSTRACT FROM AUTHOR]

BACKGROUND Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a com-Computer screen. Communication with brain-computer interfaces has been restricted by Bioextensive training requirements and limited accuracy. METHODS A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice. RESULTS On the first day of use (25 days after surgery), the neuroprosthesis achieved 99.690 Developaccuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5°6 accuracy over a contactperiod of 8.4 months after surgical implantation, and the participant used it to communicate in selfipaced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours. CONCLUSIONS In a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041). [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) have emerged as transformative tools for translating users' neural signals into commands for external devices. The urgent need for innovative treatments to enhance upper limb motor function in stroke survivors is underscored by the limitations of traditional rehabilitation methods. The development of communication and control technology for individuals with severe neuromuscular diseases, particularly stroke patients, is centered on utilizing electroencephalographic (EEG) signals to accurately decode users' intentions and operate external devices. Two healthy subjects and a stroke patient were enrolled to acquire EEG signals using the EMOTIV EPOC+ sensor. The experimental procedure involved recording five actions for both motor imagery and facial expression signals to control the 3D-printed upper limb exoskeleton. EEGLAB and BCILAB software were used for preprocessing and classification. The results showed successful EEG-based control of the exoskeleton, representing a significant advancement in assistive technology for individuals with motor impairments. The support vector machine (SVM) classifier achieved higher accuracy in both offline and online modes for both motor imaginary and facial expression tasks. The conclusion highlights the appropriateness of using EEGLAB for offline EEG data analysis and BCILAB for both offline and online analysis and classification. The integration of servo motors in the exoskeleton, allowing movements in five Degrees of Freedom (DOF), positions it as an effective rehabilitation solution for individuals with upper limb impairments. [ABSTRACT FROM AUTHOR]

Background: The rise of a new generation of intelligent neuroprostheses, brain-computer interfaces (BCI) and adaptive closed-loop brain stimulation devices hastens the clinical deployment of neurotechnologies to treat neurological and neuropsychiatric disorders. However, it remains unclear how these nascent technologies may impact the subjective experience of their users. To inform this debate, it is crucial to have a solid understanding how more established current technologies already affect their users. In recent years, researchers have used qualitative research methods to explore the subjective experience of individuals who become users of clinical neurotechnology. Yet, a synthesis of these more recent findings focusing on qualitative methods is still lacking. Methods: To address this gap in the literature, we systematically searched five databases for original research articles that investigated subjective experiences of persons using or receiving neuroprosthetics, BCIs or neuromodulation with qualitative interviews and raised normative questions. Results: 36 research articles were included and analysed using qualitative content analysis. Our findings synthesise the current scientific literature and reveal a pronounced focus on usability and other technical aspects of user experience. In parallel, they highlight a relative neglect of considerations regarding agency, self-perception, personal identity and subjective experience. Conclusions: Our synthesis of the existing qualitative literature on clinical neurotechnology highlights the need to expand the current methodological focus as to investigate also non-technical aspects of user experience. Given the critical role considerations of agency, self-perception and personal identity play in assessing the ethical and legal significance of these technologies, our findings reveal a critical gap in the existing literature. This review provides a comprehensive synthesis of the current qualitative research landscape on neurotechnology and the limitations thereof. These findings can inform researchers on how to study the subjective experience of neurotechnology users more holistically and build patient-centred neurotechnology. [ABSTRACT FROM AUTHOR]

Feeding behavior is a complex physiological process regulated by the interplay between homeostatic and hedonic feeding circuits. Among the neural structures involved, the nucleus accumbens (NAc) has emerged as a pivotal region at the interface of these two circuits. The NAc comprises distinct subregions and in this review, we focus mainly on the NAc shell (NAcSh). Homeostatic feeding circuits, primarily found in the hypothalamus, ensure the organism's balance in energy and nutrient requirements. These circuits monitor peripheral signals, such as insulin, leptin, and ghrelin, and modulate satiety and hunger states. The NAcSh receives input from these homeostatic circuits, integrating information regarding the organism's metabolic needs. Conversely, so-called hedonic feeding circuits involve all other non-hunger and -satiety processes, i.e., the sensory information, associative learning, reward, motivation and pleasure associated with food consumption. The NAcSh is interconnected with hedonics-related structures like the ventral tegmental area and prefrontal cortex and plays a key role in encoding hedonic information related to palatable food seeking or consumption. In sum, the NAcSh acts as a crucial hub in feeding behavior, integrating signals from both homeostatic and hedonic circuits, to facilitate behavioral output via its downstream projections. Moreover, the NAcSh's involvement extends beyond simple integration, as it directly impacts actions related to food consumption. In this review, we first focus on delineating the inputs targeting the NAcSh; we then present NAcSh output projections to downstream structures. Finally we discuss how the NAcSh regulates feeding behavior and can be seen as a neural hub integrating homeostatic and hedonic feeding signals, via a functionally diverse set of projection neuron subpopulations. [ABSTRACT FROM AUTHOR]

Next‐generation bioelectronic implants require miniaturization, durability, and long‐term functionality. Thin film encapsulations, prepared with inorganic or hybrid organic/inorganic designs, are essential for ensuring protection, low water permeation, adaptability, and structural strength. It is equally important to precisely measure their barrier performance, especially for in vivo use, to ensure the manufacture of reliable bioelectronics. Current monitoring solutions are not adequate: they are bulky, lack sensitivity, and are incompatible with microfabricated devices. Here, a comprehensive method is introduced to quantify the permeability of thin‐film encapsulation for bioelectronic implants both in situ and in real time. This method relies on monitoring the electrical resistance of the Mg film, which experiences corrosion due to water permeation, leading to Mg hydrolysis. An analytical model is proposed that predicts and quantifies this permeation, and is adaptable for various types of encapsulations, including hybrid multilayers. An unprecedented ultra‐low detection limit of 3 × 10−8 g m−2 d−1 at room temperature is demonstrated and the monitoring approach is validated in vivo using polyimide and poly(dimethylsiloxane)‐coated bioelectronics. [ABSTRACT FROM AUTHOR]

Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability and optimal performance of various tasks. In this report, we describe a MultiModal Vigilance (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks. The BCI tasks encompass a rapid serial visual presentation (RSVP)-based target image retrieval task and a steady-state visual evoked potential (SSVEP)-based cursor-control task. The MMV dataset includes four sessions of seven physiological signals for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), and eye movement. The MMV dataset provides data from four stages: 1) raw data, 2) pre-processed data, 3) trial data, and 4) feature data that can be directly used for vigilance estimation. We believe this dataset will achieve flexible reuse and meet the various needs of researchers. And this dataset will greatly contribute to advancing research on physiological signal-based vigilance research and estimation. [ABSTRACT FROM AUTHOR]

Limb neuroprostheses aim to restore motor and sensory functions in amputated or severely nerve‐injured patients. These devices use neural interfaces to record and stimulate nerve action potentials, creating a bidirectional connection with the nervous system. Most neural interfaces are based on standard metal microelectrodes. In this work, a new generation of neural interfaces which replaces metals with engineered graphene, called EGNITE, is tested. In vitro and in vivo experiments are conducted to assess EGNITE biocompatibility. In vitro tests show that EGNITE does not impact cell viability. In vivo, no significant functional decrease or harmful effects are observed. Furthermore, the foreign body reaction to the intraneural implant is similar compared to other materials previously used in neural interfaces. Regarding functionality, EGNITE devices are able to stimulate nerve fascicles, during two months of implant, producing selective muscle activation with about three times less current compared to larger microelectrodes of standard materials. CNAP elicited by electrical stimuli and ENG evoked by mechanical stimuli are recorded with high resolution but are more affected by decreased functionality over time. This work constitutes further proof that graphene‐derived materials, and specifically EGNITE, is a promising conductive material of neural electrodes for advanced neuroprostheses. [ABSTRACT FROM AUTHOR]

Error-related potentials (ErrPs) have attracted attention in part because of their practical potential for building brain-computer interface (BCI) paradigms. BCIs, facilitating direct communication between the brain and machines, hold great promise for brain-AI interaction. Therefore, a comprehensive understanding of ErrPs is crucial to ensure reliable BCI outcomes. In this study, we investigated ErrPs in the context of the “guess what I am thinking” paradigm. 23 healthy participants were instructed to imagine an object from a predetermined set, while an algorithm randomly selected another object that was either the same as or different from the imagined object. We recorded and analyzed the participants’ EEG activity to capture their mental responses to the algorithm’s “predictions”. The study identified components distinguishing correct from incorrect responses. It discusses their nature and how they differ from ErrPs extensively studied in other BCI paradigms. We observed pronounced variations in the shape of ErrPs across different stimulus sets, underscoring the significant influence of visual stimulus appearance on ErrP peaks. These findings have implications for designing effective BCI systems, especially considering the less conventional BCI paradigm employed. They emphasize the necessity of accounting for stimulus factors in BCI development. [ABSTRACT FROM AUTHOR]

The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCI) are systems able to respond to a user's intent based on physiological information acquired directly from his/her brain. BCI-based games employ those physiological data to control or alter the game world, providing not only the possibility of greater immersion for the player, but also allowing virtually any person, regardless of physical disabilities, to play the game, allowing many research possibilities for the field of Human-Computer Interaction (HCI). However, being an interdisciplinary field, research with BCI-based games is often conducted by researchers from different areas, making it difficult to organize and compare studies involving those games. In this context, this article presents a model-based taxonomy for games controlled by electroencephalography (EEG), a specific case of non-invasive modality for BCI systems. The taxonomy, called CoDIS, was constructed after surveying the literature and considering four aspects of EEG-controlled games, i.e., Concept, Design, Implementation and Study, each with different dimensions and attributes to represent a total of 48 characteristics of such games. Each of these dimensions was then described considering the current state of the field, and the usefulness of the taxonomy was demonstrated based on examples from the literature. The taxonomy may be used by researchers, designers and developers to classify, identify, implement and compare EEG-based games based on the dimensions of the taxonomy. [ABSTRACT FROM AUTHOR]

A brain–computer interface (BCI) bridges brain and computer activities through the computer engineering technique of capturing and analysing a person's biological and neurological response signals. This article reviews 37 empirical studies published in the past five years regarding the application of BCIs in mainstream education contexts for students' learning regulation. The characteristics of the research methodology, technologies, targeted application scenarios and main outcomes of the reviewed studies are presented in detail and summarised. The results show that most of the research in this area has concentrated on the effects of neurofeedback on the learning regulation process, optimising data-processing algorithms, developing criteria, and designing BCI-based instructional environments. Neurofeedback- and biofeedback-based BCI approaches can help to monitor learning states and have positive effects on learning regulation. The paper also discusses the future development of BCIs as an effective technique for learning analysis and regulation, and it proposes a prototype of neurofeedback- and biofeedback-based BCI learning regulation systems and their application scenarios. By highlighting the current applications and possible future trends of the use of BCIs in mainstream education and identifying the challenges involved, this review can guide the development of smart education in the age of artificial intelligence. [ABSTRACT FROM AUTHOR]

A growing number of individuals live with medical conditions and injuries that render them minimally communicative. Assessing their level of consciousness and awareness is a major challenge that has profound implications for care decisions and their relationships. Resonance: a novel brain-computer interface assemblage, is designed to detect and augment expressions of consciousness in minimally communicative individuals. Resonance consists of (1) high-density EEG features that vary with states of consciousness; (2) sound; and (3) therapeutic clowns. Seven EEG features of consciousness are calculated in real time and mapped to sonic output. Therapeutic clowns use multisensory improvisational play to interact with these sonified brain features to create interpersonal connections with minimally communicative individuals. Resonance has the potential to reveal real-time variations in an individual's level of consciousness, which may create an entirely new form of interpersonal interaction with minimally communicative persons. [ABSTRACT FROM AUTHOR]

Purpose: Coherence analysis in electroencephalography (EEG) allows measurement of the degree of consistency of amplitude between pairs of electrodes. Theoretically, disconnective epilepsy surgery should decrease coherence between corresponding areas. The study aimed to evaluate postoperative changes in interhemispheric coherence values after corpus callosotomy (CC). Methods: Non-lesional, drug-resistant, generalized epilepsy patients who underwent total CC were retrospectively collected. To evaluate coherence, we divided the scalp interictal EEG into "baseline" and "discharge" states after excluding periods with artifacts. Interhemispheric coherence values were obtained between eight pairs of symmetrically opposite scalp electrodes in six different frequency bands. We analyzed both pre- and postoperative EEG sessions and calculated the percentage of difference (POD) in coherence values. Results: We collected 13 patients and analyzed 2496 interhemispheric coherence values. Preoperative coherence values differed significantly between baseline and discharge states (p = 0.0003), but postoperative values did not (p = 0.11). For baseline state, coherence values were decreased after CC and median POD was − 22.3% (p < 0.0001). Delta frequency showed the most decreased POD (-44.3%, p = 0.0009). Median POD was lowest in the Fp1-Fp2 pair of electrodes. For discharge state, coherence values were decreased after CC and median POD was − 24.7% (p < 0.0001). Delta frequency again showed the most decreased POD (-55.9%, p = 0.0016). Median POD was lowest in the F7-F8 pair. Conclusion: After total CC, interhemispheric coherence decreased significantly in both baseline and discharge states. The most decreased frequency band was the delta band, which may be used as a representative frequency band in future studies. [ABSTRACT FROM AUTHOR]

Distributions of electroencephalogram (EEG) data vary greatly across different subjects. It is a very important issue how to generalize models across subjects. In this paper, an algorithm is proposed to build high-performance cross-subject motor-imagery brain–computer interfaces (BCIs) for a new subject. First, a novel distance metric is proposed to quantify the joint distribution discrepancy (JDD) between data from different subjects. It gives better evaluations for discrepancies between different distributions than conventional probabilistic metrics. Moreover, it can be extended to design many novel algorithms. Second, a support vector machine combined with JDD (JDMSVM) is proposed for cross-subject classification. For dataset dataIVa, the JDMSVM runs best under 9 out of 15 situations and averagely outperforms counterparts by 10.1%, 9.5%, 3.2% and 1.7%, respectively. For GigaDataset, JDMSVM runs best under 8 of 12 conditions. It averagely outperforms its counterparts by 10.4%, 5.3%, 2.7% and 2.4%, respectively. The experiments demonstrate that the proposed algorithm is effective and competitive for cross-subject BCI. [ABSTRACT FROM AUTHOR]

The rapidly advancing field of brain-computer (BCI) and brain-to-brain interfaces (BBI) is stimulating interest across various sectors including medicine, entertainment, research, and military. The developers of large-scale brain-computer networks, sometimes dubbed 'Mindplexes' or 'Cloudminds', aim to enhance cognitive functions by distributing them across expansive networks. A key technical challenge is the efficient transmission and storage of information. One proposed solution is employing blockchain technology over Web 3.0 to create decentralised cognitive entities. This paper explores the potential of a decentralised web for coordinating large brain-computer constellations, and its associated benefits, focusing in particular on the conceptual and ethical challenges this innovation may pose pertaining to (1) Identity, (2) Sovereignty (encompassing Autonomy, Authenticity, and Ownership), (3) Responsibility and Accountability, and (4) Privacy, Safety, and Security. We suggest that while a decentralised web can address some concerns and mitigate certain risks, underlying ethical issues persist. Fundamental questions about entity definition within these networks, the distinctions between individuals and collectives, and responsibility distribution within and between networks, demand further exploration. [ABSTRACT FROM AUTHOR]

• Patients with postoperative delirium are twice more likely to have significant intraoperative Electroencephalography (EEG) changes, especially those with persistent EEG changes. • The probability of not having postoperative delirium is 78.7% in patients without a significant intraoperative EEG changes. • Patient's age, increased length of procedure and elective procedure status are still significantly associated with postoperative delirium after adjusting for confounders. To determine the utility of electroencephalography (EEG) in predicting postoperative delirium (POD) in patients who underwent cardiovascular surgeries with EEG monitoring. A total of 1161 patients who underwent cardiovascular surgeries with EEG monitoring were included in the study, and their data were retrospectively reviewed. POD assessment was done utilizing Intensive Care Delirium Screening Checklist (ICDSC). Patients with a score of > 4 on ICDSC were diagnosed with POD. Of 1161 patients, 131 patients had EEG changes and 56 (42.74%) of 131 patients experienced POD. Of 1030 patients without EEG changes, 219 (21.26%) experienced POD. EEG showed specificity of 91.5% and negative predictive value of 78.7% in detecting POD. On multivariable analysis, EEG changes showed a strong association with POD (OR adj 1.97 CI (1.30–2.99), p = 0.001) with persistent EEG changes showing even a higher risk of developing POD (OR adj 2.65 (1.43–4.92), p = 0.002). EEG change has specificity of 91.5% emphasizing the need for its implementation as a diagnostic tool for predicting POD. Patients with POD are two times more likely to experience significant EEG changes, especially persistent EEG changes when undergoing cardiovascular surgeries. Intraoperative EEG can detect POD, and EEG changes based therapeutic interventions can mitigate POD. [ABSTRACT FROM AUTHOR]

• We assessed the ability of EEG indices to predict cerebral edema and clinical outcomes in acute ischemic stroke patients. • The frontal delta/alpha power ratio showed excellent predictive ability for the prognosis of stroke patients post-thrombectomy. • Quantitative EEG can help identify patients at risk of severe neurological complications that can impact functional recovery. We aimed to determine whether quantitative electroencephalography (QEEG) measures have predictive value for cerebral edema (CED) and clinical outcomes in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion who underwent mechanical thrombectomy (MT). A total of 105 patients with AIS in the anterior circulation were enrolled in this prospective study. The occurrence and severity of CED were assessed through computed tomography conducted 24 h after MT. Clinical outcomes were evaluated based on early neurological deterioration (END) and 3-month functional status, as measured by the modified Rankin scale (mRS). Electroencephalography (EEG) recordings were performed 24 h after MT, and QEEG indices were calculated from the standard 16 electrodes and 2 frontal channels (F3-C3, F4-C4). The delta/alpha ratio (DAR), the (delta + theta) / (alpha + beta) ratio (DTABR), and relative delta power were averaged over all electrodes (global) and the F3-C3 and F4-C4 channels (frontal). The predictive effect and value of QEEG indices for CED and clinical outcomes were assessed using ordinal and logistic regression models, as well as receiver operating characteristic (ROC) curves. Significantly, both global and frontal DAR were found to be associated with the severity of CED, END, and poor functional outcomes at 90 days, while global and frontal DTABR and relative delta power were not associated with outcomes. In ROC analysis, the best predictive effect was observed in frontal DAR, with an area under the curve of approximately 0.80. It exhibited approximately 75% sensitivity and 71% specificity for radiological and clinical outcomes when a threshold of 3.3 was used. QEEG techniques may be considered an efficient bedside monitoring method for assessing treatment efficacy, identifying patients at higher risk of severe CED and END, and predicting long-term functional outcomes. QEEG can help identify patients at risk of severe neurological complications that can impact long-term functional recovery in AIS patients who underwent MT. [ABSTRACT FROM AUTHOR]

Training a robust classifier for a motor imagery-based brain-computer interface (MI-BCI) system typically requires a substantial amount of time to collect calibration data, which can be a burdensome task for participants. To enhance the classifier performance while reducing the effort during the training phase, this paper proposes a domain adaptation algorithm based on manifold embedding (eSPDA) by combining the domain adaptation approach with a dimensionality reduction framework derived from the common spatial patterns (CSP). Specifically, the CSP spatial filtering theory is construed as the dimensionality reduction in Riemannian manifold with maximum intra-class variance or maximum inter-class distance. Based on the principle of maximum inter-class distance, the labeled source data is embedded into a more discriminative submanifold, where the principal characteristics of the unlabeled target subject are preserved by the rule of maximum intra-class variance. Meanwhile, the joint distribution alignment is integrated into the framework to minimize the distribution divergences across subjects. The results on two datasets demonstrate that eSPDA outperforms several state-of-the-art domain adaptation methods, with the average accuracies 70.35% and 80.67% on BCI Competition IV dataset IIa and BCI Competition IV dataset IVa, respectively. This research indicates that eSPDA has potentials to reduce the labeling effort, resulting in calibration time and effort savings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliablymeasuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data. [ABSTRACT FROM AUTHOR]

Despite the great success achieved by recently developed neural interfaces, multi‐site monitoring and regulating neural activities with high spatial and temporal selectivity remain a challenge. Here, an implantable, remotely controllable, fiber‐based ferromagnetic system permitting 3D navigation, omnidirectional steering, multiplexing neural recording, and modulation is presented. A family of fibers is fabricated that allows for the heterogeneous integration of ferromagnetic, optical, microfluidic, electrical, and electrochemical components into the proposed multifunctional neural interface. Coupling with magnetic actuation, it is demonstrated that this system can enable optical and chemical modulation of local neural activities across multiple distant regions in rodent brains, while simultaneously allowing the real‐time monitoring of neural electrophysiological and chemical activities. Furthermore, to systematically identify altered patterns of behaviors, brain activities and dopamine release during optogenetic modulation of specific nuclei in Parkinsonian animals this platform is employed. This proposed system with high spatial selectivity, multiplexing sensing and multimodal manipulating capabilities offers a versatile platform to advance both fundamental neuroscience studies and translational applications in neurologic disease treatments. [ABSTRACT FROM AUTHOR]

Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions. Steady state visually evoked potentials enable EEG vision-based brain computer interfaces. We show that it is possible to encode information distributed across 100+ frequencies. We encode images and perform simple physical computing classification tasks. Connecting more than one in brain in series improves the classification capability. [ABSTRACT FROM AUTHOR]

Introduction: Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time- consuming calibration and a large number of training samples. Methods: To address these challenges, this study proposes a transfer learning- based approach that uses a convolutional neural network for high-level feature extraction, followed by Euclidean space data alignment to ensure similar distributions of extracted features. Furthermore, a source selection technique based on the Euclidean distance metric was applied to measure the distance between each source feature sample and a reference point from the target domain. The samples with the lowest distance were then chosen to increase the similarity between source and target datasets. Finally, the transferred features are applied to a discriminative restricted Boltzmann machine classifier for P300 detection. Results: The proposed method was evaluated on the state-of-the-art BCI Competition III dataset II and rapid serial visual presentation dataset. The results demonstrate that the proposed technique achieves an average accuracy of 97% for both online and offline after 15 repetitions, which is comparable to the state- of-the-artmethods. Notably, the proposed approach requires <½ of the training samples needed by previous studies. Discussion: Therefore, this technique offers an efficient solution for developing ERP-based BCIs with robust performance against reduced a number of training data. [ABSTRACT FROM AUTHOR]

Electrocorticography is an established neural interfacing technique wherein an array of electrodes enables large-area recording from the cortical surface. Electrocorticography is commonly used for seizure mapping however the implantation of large-area electrocorticography arrays is a highly invasive procedure, requiring a craniotomy larger than the implant area to place the device. In this work, flexible thin-film electrode arrays are combined with concepts from soft robotics, to realize a large-area electrocorticography device that can change shape via integrated fluidic actuators. We show that the 32-electrode device can be packaged using origami-inspired folding into a compressed state and implanted through a small burr-hole craniotomy, then expanded on the surface of the brain for large-area cortical coverage. The implantation, expansion, and recording functionality of the device is confirmed in-vitro and in porcine in-vivo models. The integration of shape actuation into neural implants provides a clinically viable pathway to realize large-area neural interfaces via minimally invasive surgical techniques. Large area electrocorticography is limited by invasive craniotomies. Here, the authors combine soft robotic bioelectronics with origami inspired packaging to create a minimally invasive electrocorticography array. [ABSTRACT FROM AUTHOR]

The development of advanced neural modulation techniques is crucial to neuroscience research and neuroengineering applications. Recently, optical-based, nongenetic modulation approaches have been actively investigated to remotely interrogate the nervous system with high precision. Here, we show that a thin-film, silicon (Si)-based diode device is capable to bidirectionally regulate in vitro and in vivo neural activities upon adjusted illumination. When exposed to high-power and short-pulsed light, the Si diode generates photothermal effects, evoking neuron depolarization and enhancing intracellular calcium dynamics. Conversely, low-power and long-pulsed light on the Si diode hyperpolarizes neurons and reduces calcium activities. Furthermore, the Si diode film mounted on the brain of living mice can activate or suppress cortical activities under varied irradiation conditions. The presented material and device strategies reveal an innovated optoelectronic interface for precise neural modulations. [ABSTRACT FROM AUTHOR]

Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain-machine interface that initiated task trials based on the magnitude of prefrontal-hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain-machine interfacing. [ABSTRACT FROM AUTHOR]

Conventional electrodes are of significant challenges in acquiring bioelectric signals concerning a high signal‐to‐noise ratio (SNR), long‐term and comfortable wear, low motional noise, etc. To resolve these issues, silk fibroin (SF) materials are adopted to create flexible microneedle array electrodes (FMNA‐electrode) based on meso‐engineering. The research approaches involve constructing flexible conductive electrodes from non‐conductive and bristle‐regenerated SF. By controlling the molecular interchain nucleation in the refolding process of unfolded regenerated SF molecules, this goal can be achieved. In this process, silver nanowires (Ag NWs) are adopted as meso‐dopants to reconstruct the meso‐electronic structure of SF films at the surface, while polyurethane (PU) is selected as molecular templates to facilitate the molecular nucleation in the refolding of SF molecules. This leads to the occurrence of SF‐PU meso‐hybridization, giving rise to more flexible meso‐hybridized SF. The SF‐PU FMNA‐electrode demonstrates biocompatibility, comfort wearing, long‐term monitoring capability, low motional noise, and a high SNR in the application of collecting ECG, EMG, and EOG signals. The outperformance of SF‐PU FMNA‐electrode is superior to both conventional wet and commercially available dry electrodes, paving the way for the next generation of flexible, skin‐friendly, durable, and high‐performance health monitoring devices and brain‐machine interfaces. [ABSTRACT FROM AUTHOR]

The classification performance of endogenous electroencephalogram (EEG) brain-computer interfaces (BCIs) can be improved by hybridizing the paradigm through the use of commands from multiple paradigms. Hybrid paradigms using motor imagery (MI) and speech imagery (SI) have shown promise, but there is a lack of research into: i) their effectiveness when compared to pure MI and SI for multiclass problems, and ii) automated command selection. This study investigates multiclass MI and SI hybrid paradigms and compares the results to those obtained using pure MI and SI. Performance was assessed using F1 score and accuracy. The performances of all possible hybrid paradigm designs were assessed. The analysis indicated that hybridization does not always guarantee improved performance when compared to the pure paradigms, and there is inter-subject variation in the best paradigm. This confirmed the need for automated subject-specific hybrid paradigm designs. An automated hybrid paradigm selection technique using successive halving (SH) for expedited computational times was developed and results were compared to those obtained using a standard grid search. The SH approach resulted in an improvement in F1 score of 21.09% and 36.86% compared to MI and SI and led to a reduction in computational times of 82.80% compared to grid search. [ABSTRACT FROM AUTHOR]

Affect-biased attention is the phenomenon of prioritizing attention to emotionally salient stimuli and away fromgoal-directed stimuli. It is thought that affect-biased attention to emotional stimuli is a driving factor in the development of depression. This effect has been well-studied in adults, but research shows that this is also true during adolescence, when the severity of depressive symptoms are correlated with the magnitude of affect-biased attention to negative emotional stimuli. Prior studies have shown that trainings to modify affect-biased attention may ameliorate depression in adults, but this research has also been stymied by concerns about reliability and replicability. This study describes a clinical application of augmented reality-guided EEG-based attention modification ("AttentionCARE") for adolescents who are at highest risk for future depressive disorders (i.e., daughters of depressed mothers). Our results (n = 10) indicated that the AttentionCARE protocol can reliably and accurately provide neurofeedback about adolescent attention to negative emotional distractors that detract from attention to a primary task. Through several within and cross-study replications, our work addresses concerns about the lack of reliability and reproducibility in brain-computer interface applications, offering insights for future interventions to modify affect-biased attention in high-risk adolescents. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodResultConclusionTo enable the codesign of a training framework for brain-computer interfaces for augmentative and alternative communications access (BCI-AAC), the aim of this study is to evaluate the initial BCI-AAC training needs and preferred learning strategies of speech-language pathologists (SLPs) with AAC experience.Eleven SLPs employed across a broad range of settings completed a semi-structured interview. A grounded theory approach alongside peer debriefing and review, member checking, and triangulation procedures were utilised for thematic analysis to help ensure data reliability and credibility.Regarding critical training needs, SLPs identified the subthemes of (a) personalisation of intervention, (b) technical aspects, (c) BCI-AAC system types and access, and (d) how to support stakeholders in BCI-AAC implementation. Regarding learning strategy preferences, participants discussed (a) expert guidance and demonstrations, (b) hands-on experience, alongside (c) media and presentations.Findings present a continuum of critical training needs ranging from more foundational information to more personalised assessment and intervention consideration. These thematic results present a first step in developing a basic framework for SLP training in BCI-AAC to utilise and build from as technology development continues, and provides an important initial starting point for the codesign of clinically focused BCI-AAC trainings. [ABSTRACT FROM AUTHOR]

Integration of bioelectronic devices in clinical practice is expanding rapidly, focusing on conditions ranging from sensory to neurological and mental health disorders. While platinum (Pt) electrodes in neuromodulation devices such as cochlear implants and deep brain stimulators have shown promising results, challenges still affect their long‐term performance. Key among these are electrode and device longevity in vivo, and formation of encapsulating fibrous tissue. To overcome these challenges, organic conductors with unique chemical and physical properties are being explored. They hold great promise as coatings for neural interfaces, offering more rapid regulatory pathways and clinical implementation than standalone bioelectronics. This study provides a comprehensive review of the potential benefits of organic coatings in neuromodulation electrodes and the challenges that limit their effective integration into existing devices. It discusses issues related to metallic electrode use and introduces physical, electrical, and biological properties of organic coatings applied in neuromodulation. Furthermore, previously reported challenges related to organic coating stability, durability, manufacturing, and biocompatibility are thoroughly reviewed and proposed coating adhesion mechanisms are summarized. Understanding organic coating properties, modifications, and current challenges of organic coatings in clinical and industrial settings is expected to provide valuable insights for their future development and integration into organic bioelectronics. [ABSTRACT FROM AUTHOR]

Multi-channel acquisition systems of brain neural signals can provide a powerful tool with a wide range of information for the clinical application of brain computer interfaces. High-throughput implantable systems are limited by size and power consumption, posing challenges to system design. To acquire more comprehensive neural signals and wirelessly transmit high-throughput brain neural signals, a FPGA-based acquisition system for multi-channel brain nerve signals has been developed. And the Bluetooth transmission with low-power technology are utilized. To wirelessly transmit large amount of data with limited Bluetooth bandwidth and improve the accuracy of neural signal decoding, an improved sharing run length encoding (SRLE) is proposed to compress the spike data of brain neural signal to improve the transmission efficiency of the system. The functional prototype has been developed, which consists of multi-channel data acquisition chips, FPGA main control module with the improved SRLE, a wireless data transmitter, a wireless data receiver and an upper computer. And the developed functional prototype was tested for spike detection of brain neural signal by animal experiments. From the animal experiments, it shows that the system can successfully collect and transmit brain nerve signals. And the improved SRLE algorithm has an excellent compression effect with the average compression rate of 5.94%, compared to the double run-length encoding, the FDR encoding, and the traditional run-length encoding. The developed system, incorporating the improved SRLE algorithm, is capable of wirelessly capturing spike signals with 1024 channels, thereby realizing the implantable systems of High-throughput brain neural signals. [ABSTRACT FROM AUTHOR]

Background: Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications. Methods: In response to this predicament, we introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework. By incorporating Conditional Generative Adversarial Network (CGAN) data augmentation, a cropped training strategy and a Squeeze-and-Excitation (SE) attention mechanism, EEGGAN-Net adeptly assimilates crucial features from the data, consequently enhancing classification efficacy across diverse BCI tasks. Results: The EEGGAN-Net model exhibits notable performance metrics on the BCI Competition IV-2a and IV-2b datasets. Specifically, it achieves a classification accuracy of 81.3% with a kappa value of 0.751 on the IV-2a dataset, and a classification accuracy of 90.3% with a kappa value of 0.79 on the IV-2b dataset. Remarkably, these results surpass those of four other CNN-based decoding models. Conclusions: In conclusion, the amalgamation of data augmentation and attention mechanisms proves instrumental in acquiring generalized features from EEG signals, ultimately elevating the overall proficiency of EEG signal classification. [ABSTRACT FROM AUTHOR]

Encoding artificial perceptions through brain stimulation, especially that of higher cognitive functions such as speech perception, is one of the most formidable challenges in brain-computer interfaces (BCI). Brain stimulation has been used for functional mapping in clinical practices for the last 70 years to treat various disorders affecting the nervous system, including epilepsy, Parkinson's disease, essential tremors, and dystonia. Recently, direct electrical stimulation has been used to evoke various forms of perception in humans, ranging from sensorimotor, auditory, and visual to speech cognition. Successfully evoking and fine-tuning artificial perceptions could revolutionize communication for individuals with speech disorders and significantly enhance the capabilities of brain-computer interface technologies. However, despite the extensive literature on encoding various perceptions and the rising popularity of speech BCIs, inducing artificial speech perception is still largely unexplored, and its potential has yet to be determined. In this paper, we examine the various stimulation techniques used to evoke complex percepts and the target brain areas for the input of speech-like information. Finally, we discuss strategies to address the challenges of speech encoding and discuss the prospects of these approaches. [ABSTRACT FROM AUTHOR]

Speech brain-computer interfaces aim to support communication-impaired patients by translating neural signals into speech. While impressive progress was achieved in decoding performed, perceived and attempted speech, imagined speech remains elusive, mainly due to the absence of behavioral output. Nevertheless, imagined speech is advantageous since it does not depend on any articulator movements that might become impaired or even lost throughout the stages of a neurodegenerative disease. In this study, we analyzed electrocortigraphy data recorded from 16 participants in response to 3 speech modes: performed, perceived (listening), and imagined speech. We used a linear model to detect speech events and examined the contributions of each frequency band, from delta to high gamma, given the speech mode and electrode location. For imagined speech detection, we observed a strong contribution of gamma bands in the motor cortex, whereas lower frequencies were more prominent in the temporal lobe, in particular of the left hemisphere. Based on the similarities in frequency patterns, we were able to transfer models between speech modes and participants with similar electrode locations. The authors analyze electrocortigraphy data to demonstrate a contribution of gamma oscillations and low frequency waves to imagined speech, developing a model for speech detection capable of generalizing across participants and speech modes. [ABSTRACT FROM AUTHOR]