Human emotion recognition could greatly contribute to human-computer interaction with promising applications in artificial intelligence. One of the challenges in recognition tasks is learning ...effective representations with stable performances from electroencephalogram (EEG) signals. In this article, we propose a novel deep-learning framework, named channel-fused dense convolutional network, for EEG-based emotion recognition. First, we use a 1-D convolution layer to receive weighted combinations of contextual features along the temporal dimension from EEG signals. Next, inspired by state-of-the-art object classification techniques, we employ 1-D dense structures to capture electrode correlations along the spatial dimension. The developed algorithm is capable of handling temporal dependencies and electrode correlations with the effective feature extraction from noisy EEG signals. Finally, we perform extensive experiments based on two popular EEG emotion datasets. Results indicate that our framework achieves prominent average accuracies of 90.63% and 92.58% on the SEED and DEAP datasets, respectively, which both receive better performances than most of the compared studies. The novel model provides an interpretable solution with excellent generalization capacity for broader EEG-based classification tasks.
This study proposes a novel hybrid brain-computer interface (BCI) approach for increasing the spelling speed. In this approach, the P300 and steady-state visually evoked potential (SSVEP) detection ...mechanisms are devised and integrated so that the two brain signals can be used for spelling simultaneously. Specifically, the target item is identified by 2-D coordinates that are realized by the two brain patterns. The subarea/location and row/column speedy spelling paradigms were designed based on this approach. The results obtained for 14 healthy subjects demonstrate that the average online practical information transfer rate, including the time of break between selections and error correcting, achieved using our approach was 53.06 bits/min. The pilot studies suggest that our BCI approach could achieve higher spelling speed compared with the conventional P300 and SSVEP spellers.
A wireless 64-channel ElectroCorticoGram (ECoG) recording implant named WIMAGINE has been designed for various clinical applications. The device is aimed at interfacing a cortical electrode array to ...an external computer for neural recording and control applications. This active implantable medical device is able to record neural activity on 64 electrodes with selectable gain and sampling frequency, with less than 1 μV RMS input referred noise in the 0.5 Hz - 300 Hz band. It is powered remotely through an inductive link at 13.56 MHz which provides up to 100 mW. The digitized data is transmitted wirelessly to a custom designed base station connected to a PC. The hermetic housing and the antennae have been designed and optimized to ease the surgery. The design of this implant takes into account all the requirements of a clinical trial, in particular safety, reliability, and compliance with the regulations applicable to class III AIMD. The main features of this WIMAGINE implantable device and its architecture are presented, as well as its functional performances and long-term biocompatibility results.
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•Motor imagery EEG patterns in spatial–spectral–temporal domain are simultaneously obtained.•Prior knowledge of the most contributed channels and frequency bands is provided.•The ...results are validated by online and offline simulations based on real EEG from strokes.•Neural mechanism about motor cortex reorganization of stroke patients during rehabilitation is revealed.
Stroke is one of the most common disorders among the elderly. A practical problem in stroke rehabilitation systems is that how to separate motor imagery patterns from electroencephalographic (EEG) recordings. There is a sharp decline in performance of these systems when classical algorithms, such as Common Spatial Pattern (CSP), are directly applied on stroke patients.
We propose a tensor-based scheme to detect motor imagery EEG patterns in spatial–spectral–temporal domain directly from multidimensional EEG constructed by wavelet transform method. Discriminative motor imagery EEG patterns are obtained by Fisher score strategy. Furthermore, the most contributed channel groups and frequency bands are selected from these patterns and utilized as prior knowledge for the following motor imagery tasks.
We evaluate our scheme based on EEG datasets recorded from stroke patients. The results show that our method outperforms five other traditional methods in both online and offline recognition performance.
Unlike the existing methods, motor imagery EEG patterns in spatial–spectral–temporal domain are simultaneously obtained by our method, preserving the structural information of the multi-channel time-varying EEG.
Our scheme is encouraged to be transferred to some other practical rehabilitation applications for its better performance.
Highlights • Implementation of a tactile event-related paradigm for severely paralyzed people. • No event-related potential pathology in patients with amyotrophic lateral sclerosis. • Discussion of ...implications for brain-computer interfaces in paralyzed patients.
Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared ...spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for "mental communication" on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for "yes" and to stay relaxed for "no." The change in the mean time-of-flight of photons was used to calculate the change in concentrations of oxy- and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as "yes" or "no" responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters heart rate (HR) and mean arterial pressure (MAP) were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.
Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on ...the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: ldquoAre ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?rdquo We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively, using a classifier built with data recorded up to three months earlier.
A brain-computer interface (BCI) system may allow a user to communicate by selecting one of many options. These options may be presented in a matrix. Larger matrices allow a larger vocabulary, but ...require more time for each selection. In this study, subjects were asked to perform a target detection task using matrices appropriate for a BCI. The study sought to explore the relationship between matrix size and EEG measures, target detection accuracy, and user preferences. Results indicated that larger matrices evoked a larger P300 amplitude, and that matrix size did not significantly affect performance or preferences.
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