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Conventional spike sorting and motor intention decoding algorithms are mostly implemented on an external computing device, such as a personal computer. The innovation of high-resolution and high-density electrodes to record the brain's activity at the single neuron level may eliminate the need for spike sorting altogether while potentially enabling in vivo neural decoding. This article explores the feasibility and efficient realization of in vivo decoding, with and without spike sorting. The efficiency of neural network-based models for reliable motor decoding is presented and the performance of candidate neural decoding schemes on sorted single-unit activity and unsorted multi-unit activity are evaluated. A programmable processor with a custom instruction set architecture, for the first time to the best of our knowledge, is designed and implemented for executing neural network operations in a standard 180-nm CMOS process. The processor's layout is estimated to occupy 49 mm 2 of silicon area and to dissipate 12 mW of power from a 1.8 V supply, which is within the tissue-safe operation of the brain. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) signals have demonstrated their practical application to reading human minds. Current neural decoding studies have made great progress to build subject-wise decoding models to extract and discriminate the temporal/spatial features in neural signals. In this paper, we used a compact convolutional neural network—EEGNet—to build a common decoder across subjects, which deciphered the categories of objects (faces, tools, animals, and scenes) from MEG data. This study investigated the influence of the spatiotemporal structure of MEG on EEGNet's classification performance. Furthermore, the EEGNet replaced its convolution layers with two sets of parallel convolution structures to extract the spatial and temporal features simultaneously. Our results showed that the organization of MEG data fed into the EEGNet has an effect on EEGNet classification accuracy, and the parallel convolution structures in EEGNet are beneficial to extracting and fusing spatial and temporal MEG features. The classification accuracy demonstrated that the EEGNet succeeds in building the common decoder model across subjects, and outperforms several state-of-the-art feature fusing methods. [ABSTRACT FROM AUTHOR]

While the neural bases of the earliest stages of speech categorization have been widely explored using neural decoding methods, there is still a lack of consensus on questions as basic as how wordforms are represented and in what way this word-level representation influences downstream processing in the brain. Isolating and localizing the neural representations of wordform is challenging because spoken words activate a variety of representations (e.g., segmental, semantic, articulatory) in addition to form-based representations. We addressed these challenges through a novel integrated neural decoding and effective connectivity design using region of interest (ROI)-based, source-reconstructed magnetoencephalography/electroencephalography (MEG/EEG) data collected during a lexical decision task. To identify wordform representations, we trained classifiers on words and nonwords from different phonological neighborhoods and then tested the classifiers' ability to discriminate between untrained target words that overlapped phonologically with the trained items. Training with word neighbors supported significantly better decoding than training with nonword neighbors in the period immediately following target presentation. Decoding regions included mostly right hemisphere regions in the posterior temporal lobe implicated in phonetic and lexical representation. Additionally, neighbors that aligned with target word beginnings (critical for word recognition) supported decoding, but equivalent phonological overlap with word codas did not, suggesting lexical mediation. Effective connectivity analyses showed a rich pattern of interaction between ROIs that support decoding based on training with lexical neighbors, especially driven by right posterior middle temporal gyrus. Collectively, these results evidence functional representation of wordforms in temporal lobes isolated from phonemic or semantic representations. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of decoding gestures represented by surface electromyography (sEMG) signals in the presence of variable force levels. It is an attempt that multi-task learning (MTL) is proposed to recognize gestures and force levels synchronously. First, methods of gesture recognition with different force levels are investigated. Then, MTL framework is presented to improve the gesture recognition performance and give information about force levels. Last but not least, to solve the problem that using the greedy principle in MTL, a modified pseudo-task augmentation (PTA) trajectory is introduced. Experiments conducted on two representative datasets demonstrate that compared with other methods, frequency domain information with convolutional neural network (CNN) is more suitable for gesture recognition with variable force levels. Besides, the feasibility of extracting features that are closely related to both gestures and force levels is verified via MTL. By influencing learning dynamics, the proposed PTA method can improve the results of all tasks, and make it applicable to the case where the main tasks and auxiliary tasks are clear. [ABSTRACT FROM AUTHOR]

The problem of neural decoding is essential for the realization of a neural interface. In this study, the position of the moving finger of a macaque was directly decoded through the neuron spike signals in the motor cortex, instead of relying on the synergy of the related muscle tissues around the body, also known as neural decoding. Currently, supervised learning is the most commonly employed method for this purpose. However, based on existing technologies, unsupervised learning with regression causes excessive errors. To solve this problem, weakly supervised learning (WSL) was used to correct the predicted position of the moving finger of a macaque in unsupervised training. Then, the corrected finger position was further used to train and accurately fit the weight parameters. We then utilized public data to evaluate the decoding performance of the Kalman filter (KF) and the expectation maximization (EM) algorithms in the WSL model. Unlike in previous methods, in WSL, the only available information is that the finger has moved to four areas in the plane, instead of the actual track value. When compared to the supervised models, the WSL decoding performance only differs by approximately 0.4%. This result improves by 41.3% relative to unsupervised models in the two-dimensional plane. The investigated approach overcomes the instability and inaccuracy of unsupervised learning. What's more, the method in the paper also verified that the unsupervised encoding and decoding technology of neuronal signals is related to the range of external activities, rather than having a priori specific location. [ABSTRACT FROM AUTHOR]

Humans have a remarkable capacity to mentally project themselves far ahead in time. This ability, which entails the mental simulation of events, is thought to be fundamental to deliberative decision making, as it allows us to search through and evaluate possible choices. Many decisions that humans make are foraging decisions, in which one must decide whether an available offer is worth taking, when compared to unknown future possibilities (i.e., the background). Using a translational decision-making paradigm designed to reveal decision preferences in rats, we found that humans engaged in deliberation when making foraging decisions. A key feature of this task is that preferences (and thus, value) are revealed as a function of serial choices. Like rats, humans also took longer to respond when faced with difficult decisions near their preference boundary, which was associated with prefrontal and hippocampal activation, exemplifying cross-species parallels in deliberation. Furthermore, we found that voxels within the visual cortices encoded neural representations of the available possibilities specifically following regret-inducing experiences, in which the subject had previously rejected a good offer only to encounter a low-valued offer on the subsequent trial. [ABSTRACT FROM AUTHOR]

Speech intelligibility is currently measured by scoring how well a person can identify a speech signal. The results of such behavioral measures reflect neural processing of the speech signal, but are also influenced by language processing, motivation, and memory. Very often, electrophysiological measures of hearing give insight in the neural processing of sound. However, in most methods, non-speech stimuli are used, making it hard to relate the results to behavioral measures of speech intelligibility. The use of natural running speech as a stimulus in electrophysiological measures of hearing is a paradigm shift which allows to bridge the gap between behavioral and electrophysiological measures. Here, by decoding the speech envelope from the electroencephalogram, and correlating it with the stimulus envelope, we demonstrate an electrophysiological measure of neural processing of running speech. We show that behaviorally measured speech intelligibility is strongly correlated with our electrophysiological measure. Our results pave the way towards an objective and automatic way of assessing neural processing of speech presented through auditory prostheses, reducing confounds such as attention and cognitive capabilities. We anticipate that our electrophysiological measure will allow better differential diagnosis of the auditory system, and will allow the development of closed-loop auditory prostheses that automatically adapt to individual users. [ABSTRACT FROM AUTHOR]

Recently, sparse representation has received a great deal of attention in voxel selection and decoding analysis of functional magnetic resonance imaging (fMRI) data. Due to contamination of large noise in fMRI data, the conventional sparse representation methods may not get stable results. Moreover, the selected activated brain regions may lose clustering effects and are less biologically interpretable. In order to overcome the above mentioned problems, we exploit the error-tolerant formulation of sparse representation and introduce multi-dimensional derivative constraints (smoothness constraints) in optimization. Two new methods are proposed in this paper. One is robust voxel selection with multi-dimensional constraint (RVSMDC). With the error-tolerant formulation and smoothness constraints on regression coefficients, RVSMDC is robust against noise/error and achieves clustering effects. To directly decode neural activities from fMRI data, we also proposed robust sparse decoding with multi-dimensional constraints (RSDMDC), which minimize the regression error of fMRI data to the task function with sparsity and smoothness constraints on regression coefficients. Numerical results validate the effectiveness of the two proposed methods. [ABSTRACT FROM AUTHOR]

Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information. [ABSTRACT FROM AUTHOR]

We present an approach for learning models that obtain accurate classification of data objects, collected in large-scale spatio-temporal domains. The model generation is structured in three phases: spatial dimension reduction, spatio-temporal features extraction, and feature selection. Novel techniques for the first two phases are presented, with two alternatives for the middle phase. We explore model generation based on the combinations of techniques from each phase. We apply the introduced methodology to data-sets from the Voltage-Sensitive Dye Imaging (VSDI) domain, where the resulting classification models successfully decode neuronal population responses in the visual cortex of behaving animals. VSDI is currently the best technique enabling simultaneous high spatial (10,000 points) and temporal (10 ms or less) resolution imaging from neuronal population in the cortex. We demonstrate that not only our approach is scalable enough to handle computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding abilities. The effectiveness of our methodology is further explored on a data-set from the hurricanes domain, and a promising direction, based on the preliminary results of hurricane severity classification, is revealed. [ABSTRACT FROM AUTHOR]

Neuroprosthetic devices such as a computer cursor can be controlled by the activity of cortical neurons when an appropriate algorithm is used to decode motor intention. Algorithms which have been proposed for this purpose range from the simple population vector algorithm (PVA) and optimal linear estimator (OLE) to various versions of Bayesian decoders. Although Bayesian decoders typically provide the most accurate off-line reconstructions, it is not known which model assumptions in these algorithms are critical for improving decoding performance. Furthermore, it is not necessarily true that improvements (or deficits) in off-line reconstruction will translate into improvements (or deficits) in on-line control, as the subject might compensate for the specifics of the decoder in use at the time. Here we show that by comparing the performance of nine decoders, assumptions about uniformly distributed preferred directions and the way the cursor trajectories are smoothed have the most impact on decoder performance in off-line reconstruction, while assumptions about tuning curve linearity and spike count variance play relatively minor roles. In on-line control, subjects compensate for directional biases caused by non-uniformly distributed preferred directions, leaving cursor smoothing differences as the largest single algorithmic difference driving decoder performance. [ABSTRACT FROM AUTHOR]

Perception, memory, learning, and decision making are processes carried out in the brain. The performance of such intelligent tasks is made possible by the communication of neurons through sequences of voltage pulses called spike trains. It is of great interest to have methods of extracting information from spike trains in order to learn about their relationship to behavior. In this article, we review a Bayesian approach to this problem based on state-space representations of point processes. We discuss some of the theory and we describe the way these methods are used in decoding motor cortical activity, in which the hand motion is reconstructed from neural spike trains. [ABSTRACT FROM AUTHOR]

This paper proposes a novel application of Formal Concept Analysis (FCA) to neural decoding: the semantic relationships between the neural representations of large sets of stimuli are explored using concept lattices. In particular, the effects of neural code sparsity are modelled using the lattices. An exact Bayesian approach is employed to construct the formal context needed by FCA. This method is explained using an example of neurophysiological data from the high-level visual cortical area STSa. Prominent features of the resulting concept lattices are discussed, including indications for hierarchical face representation and a product-of-experts code in real neurons. The robustness of these features is illustrated by studying the effects of scaling the attributes. [ABSTRACT FROM AUTHOR]

We trained two monkeys to draw copies of geometrical shapes (e.g. squares, triangles) using a joystick, and found that several variables describing the arm trajectories were encoded in the activity of individual prefrontal neurons (Averbeck et al. 2003). Copy trajectories were drawn as sequences of segments, identified by the serial order in which they were drawn and the shape that they together produced. Here we use linear discriminant analysis to test how well the segments of copied shapes could be decoded from the neural activity patterns of small ensembles (3–22 neurons) of simultaneously recorded cells in prefrontal cortex. Using this analysis, the proper segment (drawn by the monkey) was correctly decoded from the ensemble activity pattern during the drawing of that segment in 60–80% of the cases when the largest ensembles were considered. The information transmitted by these ensembles, as well as by single neurons, was also calculated. We found that the information transmitted by the ensembles increased on average with the number of neurons they contained. Each neuron conveyed information about multiple segments within the drawing trajectory, suggesting that neurons were 'broadly tuned' across segments and that the neural code of segment was distributed. [ABSTRACT FROM AUTHOR]