Search

Emotion regulation plays a vital role in human beings daily lives by helping them deal with social problems and protects mental and physical health. However, objective evaluation of the efficacy of emotion regulation and assessment of the improvement in emotion regulation ability at the individual level remain challenging. In this study, we leveraged neurofeedback training to design a real-time EEG-based brain-computer interface (BCI) system for users to effectively regulate their emotions. Twenty healthy subjects performed 10 BCI-based neurofeedback training sessions to regulate their emotion towards a specific emotional state (positive, negative, or neutral), while their EEG signals were analyzed in real time via machine learning to predict their emotional states. The prediction results were presented as feedback on the screen to inform the subjects of their immediate emotional state, based on which the subjects could update their strategies for emotion regulation. The experimental results indicated that the subjects improved their ability to regulate these emotions through our BCI neurofeedback training. Further EEG-based spectrum analysis revealed how each emotional state was related to specific EEG patterns, which were progressively enhanced through long-term training. These results together suggested that long-term EEG-based neurofeedback training could be a promising tool for helping people with emotional or mental disorders. [ABSTRACT FROM AUTHOR]

Recognizing human emotions based on electroencephalogram (EEG) signals has received a great deal of attentions. Most of the existing studies focused on offline analysis, and real-time emotion recognition using a brain computer interface (BCI) approach remains to be further investigated. In this paper, we proposed an EEG-based BCI system for emotion recognition. Specifically, two classes of video clips that represented positive and negative emotions were presented to the subjects one by one, while the EEG data were collected and processed simultaneously, and instant feedback was provided after each clip. Ten healthy subjects participated in the experiment and achieved a high average online accuracy of 91.5 $\pm$ ± 6.34 percent. The experimental results demonstrated that the subjects emotions had been sufficiently evoked and efficiently recognized by our system. Clinically, patients with disorder of consciousness (DOC), such as coma, vegetative state, minimally conscious state and emergence minimally conscious state, suffer from motor impairment and generally cannot provide adequate emotion expressions. Consequently, doctors have difficulty in detecting the emotional states of these patients. Therefore, we applied our emotion recognition BCI system to patients with DOC. Eight DOC patients participated in our experiment, and three of them achieved significant online accuracy. The experimental results show that the proposed BCI system could be a promising tool to detect the emotional states of patients with DOC. [ABSTRACT FROM AUTHOR]

Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs. [ABSTRACT FROM AUTHOR]

A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent model and eliminates subject-specific calibration for new users. However, building subject-independent BCIs remains difficult because electroencephalography (EEG) is highly noisy and varies by subject. In this study, we propose an invariant pattern learning method based on a convolutional neural network (CNN) and big EEG data for subject-independent P300 BCIs. The CNN was trained using EEG data from a large number of subjects, allowing it to extract subject-independent features and make predictions for new users. We collected EEG data from 200 subjects in a P300-based spelling task using two different types of amplifiers. The offline analysis showed that almost all subjects obtained significant cross-subject and cross-amplifier effects, with an average accuracy of more than 80%. Furthermore, more than half of the subjects achieved accuracies above 85%. These results indicated that our method was effective for building a subject-independent P300 BCI, with which more than 50% of users could achieve high accuracies without subject-specific calibration. [ABSTRACT FROM AUTHOR]

Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism. [ABSTRACT FROM AUTHOR]

The coma recovery scale-revised (CRS-R) behavioral scale is commonly used for the clinical evaluation of patients with disorders of consciousness (DOC). However, since DOC patients generally cannot supply stable and efficient behavioral responses to external stimulation, evaluation results based on behavioral scales are not sufficiently accurate. In this paper, we proposed a novel brain–computer interface (BCI) based on 3D stereo audiovisual stimuli to supplement object recognition evaluation in the CRS-R. During the experiment, subjects needed to follow the instructions and to focus on the target object on the screen, whereas EEG data were recorded and analyzed in real time to determine the object of focus, and the detection result was output as feedback. Thirteen DOC patients participated in the object recognition assessments using the 3D audiovisual BCI and CRS-R. None of the patients showed object recognition function in the CRS-R assessment before the BCI experiment. However, six of these DOC patients achieved accuracies that were significantly higher than the chance level in the BCI-based assessment, indicating the successful detection of object recognition function in these six patients using our 3D audiovisual BCI system. These results suggest that the BCI method may provide a more sensitive object recognition evaluation compared with CRS-R and may be used to assist clinical CRS-R for DOC patients. [ABSTRACT FROM AUTHOR]

Detecting event-related potential (ERP) is a challenging problem because of its low signal-to-noise ratio and complex spatial–temporal features. Conventional detection methods usually rely on the ensemble averaging technique, which may eliminate subtle but important information in ERP signals and lead to poor detection performance. Inspired by the good performance of discriminative restricted Boltzmann machine (DRBM) in feature extraction and classification, we propose a spatial–temporal DRBM (ST-DRBM) to extract spatial and temporal features for ERP detection. The experimental results and statistical analyses demonstrate that the proposed method is able to achieve state-of-the-art ERP detection performance. The ST-DRBM is not only an effective ERP detector, but also a practical tool for ERP analysis. Based on the proposed model, similar scalp distribution and temporal variations were found in the ERP signals of different sessions, which indicated the feasibility of cross-session ERP detection. Given its state-of-the-art performance and effective analytical technique, ST-DRBM is promising for ERP-based brain–computer interfaces and neuroscience research. [ABSTRACT FROM AUTHOR]

Visual pursuit assessment is extensively applied in the behavioral scale-based clinical examination of patients with disorders of consciousness (DOC). However, this assessment is challenging because it relies on behavioral markers, and these patients severely lack behavioral responses. Brain-computer interfaces (BCIs) may provide a potential solution to detect brain responses to external stimuli without requiring behavioral expressions. A BCI system was designed to simulate visual pursuit detection in the coma recovery scale-revised (CRS-R). The graphical user interface included four buttons, one that moved on the screen and three that did not. These buttons flashed in a random order. The patients were prompted to follow the moving button. Based on the collected electroencephalography data, the algorithm determined whether the patient focused on the moving target. Among the 14 DOC patients who participated in the assessments based on the BCI system and the CRS-R, four patients exhibited visual pursuit, and three were nonresponsive in both assessments. More importantly, seven patients who did not exhibit visual pursuit in CRS-R were detected to be responsive to the moving target stimuli in the BCI assessment. Furthermore, five out of seven recovered consciousness to some degree and showed visual pursuit in the second CRS-R assessment. The proposed BCI system is better able to detect visual pursuit than the behavioral scale-based assessment and thus can assist in clinically evaluating the challenging population of DOC patients. [ABSTRACT FROM AUTHOR]

Objective: This study proposes an event-related potential (ERP) brain-computer interface (BCI)-based environmental control system that integrates household electrical appliances, a nursing bed, and an intelligent wheelchair to provide daily assistance to paralyzed patients with severe spinal cord injuries (SCIs). Methods: An asynchronous mode is used to switch the environmental control system on or off or to select a device (e.g., a TV) for achieving self-paced control. In the asynchronous mode, we introduce several pseudo-keys and a verification mechanism to effectively reduce the false operation rate. By contrast, when the user selects a function of the device (e.g., a TV channel), a synchronous mode is used to improve the accuracy and speed of BCI detection. Two experiments involving six SCI patients were conducted separately in a nursing bed and a wheelchair, and the patients were instructed to control the nursing bed, the wheelchair, and household electrical appliances (an electric light, an air conditioner, and a TV). Results: The average false rate of BCI commands in the control state was 10.4%, whereas the average false operation ratio was 4.9% (a false BCI command might not necessarily results in a false operation according to our system design). During the idle state, there was an average of 0.97 false positives/min, which did not result in any false operations. Conclusion: All SCI patients could use the proposed ERP BCI-based environmental control system satisfactorily. Significance: The proposed ERP-based environmental control system could be used to assist patients with severe SCIs in their daily lives. [ABSTRACT FROM AUTHOR]

The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A feasible solution is to integrate a brain–computer interface (BCI) with automated navigation techniques. This paper presents a brain-controlled intelligent wheelchair with the capability of automatic navigation. Using an autonomous navigation system, candidate destinations and waypoints are automatically generated based on the existing environment. The user selects a destination using a motor imagery (MI)-based or P300-based BCI. According to the determined destination, the navigation system plans a short and safe path and navigates the wheelchair to the destination. During the movement of the wheelchair, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be substantially alleviated. Furthermore, our system can adapt to changes in the environment. Two experiments based on MI and P300 were conducted to demonstrate the effectiveness of our system. [ABSTRACT FROM PUBLISHER]

During the development of a brain-computer interface, it is beneficial to exploit information in multiple electrode signals. However, a small channel subset is favored for not only machine learning feasibility, but also practicality in commercial and clinical BCI applications. An embedded channel selection approach based on grouped automatic relevance determination is proposed. The proposed Gaussian conjugate group-sparse prior and the embedded nature of the concerned Bayesian linear model enable simultaneous channel selection and feature classification. Moreover, with the marginal likelihood (evidence) maximization technique, hyper-parameters that determine the sparsity of the model are directly estimated from the training set, avoiding time-consuming cross-validation. Experiments have been conducted on P300 speller BCIs. The results for both public and in-house datasets show that the channels selected by our techniques yield competitive classification performance with the state-of-the-art and are biologically relevant to P300. [ABSTRACT FROM AUTHOR]

Goal: Motor imagery-related mu/beta rhythms, which can be voluntarily modulated by subjects, have been widely used in EEG-based brain computer interfaces (BCIs). Moreover, it has been suggested that motor imagery-specific EEG differences can be enhanced by feedback training. However, the differences observed in the EEGs of naive subjects are typically not sufficient to provide reliable EEG control and thus result in unintended feedback. Such feedback can frustrate subjects and impede training. In this study, a hybrid BCI paradigm combining motor imagery and steady-state visually evoked potentials (SSVEPs) has been proposed to provide effective continuous feedback for motor imagery training. Methods: During the initial training sessions, subjects must focus on flickering buttons to evoke SSVEPs as they perform motor imagery tasks. The output/feedback of the hybrid BCI is based on hybrid features consisting of motor imagery- and SSVEP-related brain signals. In this context, the SSVEP plays a more important role than motor imagery in generating feedback. As the training progresses, the subjects can gradually decrease their visual attention to the flickering buttons, provided that the feedback is still effective. In this case, the feedback is mainly based on motor imagery. Results: Our experimental results demonstrate that subjects generate distinguishable brain patterns of hand motor imagery after only five training sessions lasting approximately 1.5 h each. Conclusion: The proposed hybrid feedback paradigm can be used to enhance motor imagery training. Significance: This hybrid BCI system with feedback can effectively identify the intentions of the subjects. [ABSTRACT FROM PUBLISHER]

Practical brain–computer interface (BCI) systems should require only low training effort for the user, and the algorithms used to classify the intent of the user should be computationally efficient. However, due to inter- and intra-subject variations in EEG signal, intermittent training/calibration is often unavoidable. In this paper, we present an online semi-supervised P300 BCI speller system. After a short initial training (around or less than 1 min in our experiments), the system is switched to a mode where the user can input characters through selective attention. In this mode, a self-training least squares support vector machine (LS-SVM) classifier is gradually enhanced in back end with the unlabeled EEG data collected online after every character input. In this way, the classifier is gradually enhanced. Even though the user may experience some errors in input at the beginning due to the small initial training dataset, the accuracy approaches that of fully supervised method in a few minutes. The algorithm based on LS-SVM and its sequential update has low computational complexity; thus, it is suitable for online applications. The effectiveness of the algorithm has been validated through data analysis on BCI Competition III dataset II (P300 speller BCI data). The performance of the online system was evaluated through experimental results on eight healthy subjects, where all of them achieved the spelling accuracy of 85\% or above within an average online semi-supervised learning time of around 3 min. [ABSTRACT FROM PUBLISHER]

A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) can operate as an asynchronous brain switch. When SSVEP is detected with the “on/off” button flickering at a fixed frequency, the subject is identified as in the control state. Otherwise, he is in the idle state. Generally, the detection of the idle/control state is based on a predefined threshold, which is related to power. However, due to the variability of the electroencephalogram (EEG) signal, it is difficult to find an optimal threshold to achieve a high true-positive rate (TPR) in the control state while maintaining a low false-positive rate (FPR) in the idle state. In this paper, a novel pseudo-key-based approach is presented for better discriminating the control and idle states. A dedicated “on/off” button (target key) and several additional buttons (pseudo-keys) are displayed on the graphical user interface (GUI), and all of these buttons flash at different frequencies. The control state is identified from the EEG signal under two conditions. The first is a common thresholding condition, where the power ratio of the target key frequency component to a certain neighboring frequency band is above a predefined threshold. The second is a comparison condition, where the power of the target key frequency component is higher than any of the pseudo-keys. The effectiveness of the proposed approach is validated by several experiments. Further analysis shows that introducing the pseudo-keys can significantly reduce the probability that the SSVEP will be detected in response to the flickering target key in the idle state without substantially affecting the detection in the control state, providing strong evidence in support of our approach. [ABSTRACT FROM AUTHOR]

To control a cursor on a monitor screen, a user generally needs to perform two tasks sequentially. The first task is to move the cursor to a target on the monitor screen (termed a 2-D cursor movement), and the second task is either to select a target of interest by clicking on it or to reject a target that is not of interest by not clicking on it. In a previous study, we implemented the former function in an EEG-based brain–computer interface system using motor imagery and the P300 potential to control the horizontal and vertical cursor movements, respectively. In this study, the target selection or rejection functionality is implemented using a hybrid feature from motor imagery and the P300 potential. Specifically, to select the target of interest, the user must focus his or her attention on a flashing button to evoke the P300 potential, while simultaneously maintaining an idle state of motor imagery. Otherwise, the user performs left-/right-hand motor imagery without paying attention to any buttons to reject the target. Our data analysis and online experimental results validate the effectiveness of our approach. The proposed hybrid feature is shown to be more effective than the use of either the motor imagery feature or the P300 feature alone. Eleven subjects attended our online experiment, in which a trial involved sequential 2-D cursor movement and target selection. The average duration of each trial and average accuracy of target selection were 18.19 s and 93.99\%, respectively, and each target selection or rejection event was performed within 2 s. [ABSTRACT FROM PUBLISHER]

Detecting P300 signals from electroencephalography (EEG) is the key to establishing a P300 speller, which is a type of brain–computer interface (BCI) system based on the oddball paradigm that allows users to type messages simply by controlling eye-gazes. The convolutional neural network (CNN) is an approach that has achieved good P300 detection performances. However, the standard CNN may be prone to overfitting and the convergence may be slow. To address these issues, we develop a novel CNN, termed BN 3 , for detecting P300 signals, where Batch Normalization is introduced in the input and convolutional layers to alleviate over-fitting, and the rectified linear unit (ReLU) is employed in the convolutional layers to accelerate training. Since our model is fully data-driven, it is capable of automatically capturing the discriminative spatio-temporal features of the P300 signal. The results obtained on previous BCI competition P300 data sets show that BN 3 both achieves the state-of-the-art character recognition performance and that it outperforms existing detection approaches with small flashing epoch numbers. BN 3 can be used to improve the character recognition performance in P300 speller systems. [ABSTRACT FROM AUTHOR]