Fully portable and wireless universal brain-machine interfaces enabled by flexible scalp electronics and deep-learning algorithm

Variation in human brains creates difficulty in implementing electroencephalography (EEG) into universal brain-machine interfaces (BMI). Conventional EEG systems typically suffer from motion artifacts, extensive preparation time, and bulky equipment, while existing EEG classification methods require training on a per-subject or per-session basis. Here, we introduce a fully portable, wireless, flexible scalp electronic system, incorporating a set of dry electrodes and flexible membrane circuit. Time domain analysis using convolutional neural networks allows for an accurate, real-time classification of steady-state visually evoked potentials on the occipital lobe. Simultaneous comparison of EEG signals with two commercial systems captures the improved performance of the flexible electronics with significant reduction of noise and electromagnetic interference. The two-channel scalp electronic system achieves a high information transfer rate (122.1 ± 3.53 bits per minute) with six human subjects, allowing for a wireless, real-time, universal EEG classification for an electronic wheelchair, motorized vehicle, and keyboard-less presentation

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Optimization of a hardware/software coprocessing platform for EEG eyeblink detection and removal

The feasibility of implementing a real-time system for removing eyeblink artifacts from electroencephalogram (EEG) recordings utilizing a hardware/software coprocessing platform was investigated. A software based wavelet and independent component analysis (ICA) eyeblink detection and removal process was extended to enable variation in its processing parameters. Exploiting the efficiency of hardware and the reconfigurability of software, it was ported to a field programmable gate array (FPGA) development platform which was found to be capable of implementing the revised algorithm, although not in real-time. The implemented hardware and software solution was applied to a collection of both simulated and clinically acquired EEG data with known artifact and waveform characteristics to assess its speed and accuracy. Configured for optimal accuracy in terms of minimal false positives and negatives as well as maintaining the integrity of the underlying EEG, especially when encountering EEG waveform patterns with an appearance similar to eyeblink artifacts, the system was capable of processing a 10 second EEG epoch in an average of 123 seconds. Configured for efficiency, but with diminished accuracy, the system required an average of 34 seconds. Varying the ICA contrast function showed that the gaussian nonlinearity provided the best combination of reliability and accuracy, albeit with a long execution time. The cubic nonlinearity was fast, but unreliable, while the hyperbolic tangent contrast function frequently diverged. It is believed that the utilization of programmable logic with increased logic capacity and processing speed may enable this approach to achieve the objective of real-time operation

Real time depth of anaesthesia monitoring through electroencephalogram (EEG) signal analysis based on Bayesian method and analytical technique

The electroencephalogram (EEG) signal from the brain is used for analysing brain abnormality, diseases, and monitoring patient conditions during surgery. One of the applications of the EEG signals analysis is real-time anaesthesia monitoring, as the anaesthetic drugs normally targeted the central nervous system. Depth of anaesthesia has been clinically assessed through breathing pattern, heart rate, arterial blood pressure, pupil dilation, sweating and the presence of movement. Those assessments are useful but are an indirect-measurement of anaesthetic drug effects. A direct method of assessment is through EEG signals because most anaesthetic drugs affect neuronal activity and cause a changed pattern in EEG signals. The aim of this research is to improve real-time anaesthesia assessment through EEG signal analysis which includes the filtering process, EEG features extraction and signal analysis for depth of anaesthesia assessment. The first phase of the research is EEG signal acquisition. When EEG signal is recorded, noises are also recorded along with the brain waves. Therefore, the filtering is necessary for EEG signal analysis. The filtering method introduced in this dissertation is Bayesian adaptive least mean square (LMS) filter which applies the Bayesian based method to find the best filter weight step for filter adaptation. The results show that the filtering technique is able to remove the unwanted signals from the EEG signals. This dissertation proposed three methods for EEG signal features extraction and analysing. The first is the strong analytical signal analysis which is based on the Hilbert transform for EEG signal features' extraction and analysis. The second is to extract EEG signal features using the Bayesian spike accumulation technique. The third is to apply the robust Bayesian Student-t distribution for real-time anaesthesia assessment. Computational results from the three methods are analysed and compared with the recorded BIS index which is the most popular and widely accepted depth of anaesthesia monitor. The outcomes show that computation times from the three methods are leading the BIS index approximately 18-120 seconds. Furthermore, the responses to anaesthetic drugs are verified with the anaesthetist's documentation and then compared with the BIS index to evaluate the performance. The results indicate that the three methods are able to extract EEG signal features efficiently, improve computation time, and respond faster to anaesthetic drugs compared to the existing BIS index

Classification of motor imaginary EEG signals using machine learning

Brain Computer Interface (BCI) is a term that was first introduced by Jacques Vidal in the 1970s when he created a system that can determine the human eye gaze direction, making the system able to determine the direction a person want to go or move something to using scalp-recorded visual evoked potential (VEP) over the visual cortex. Ever since that time, many researchers where captivated by the huge potential and list of possibilities that can be achieved if simply a digital machine can interpret human thoughts. In this work, we explore electroencephalography (EEG) signal classification, specifically for motor imagery (MI) tasks. Classification of MI tasks can be carried out by using machine learning and deep learning models, yet there is a trade between accuracy and computation time that needs to be maintained. The objective is to create a machine learning model that can be optimized for real-time classification while having a relatively acceptable classification accuracy. The proposed model relies on common spatial patter (CSP) for feature extraction as well as linear discriminant analysis (LDA) for classification. With simple pre-processing stage and a proper selection of data for training the model proved to have a balanced accuracy of +80% while maintaining small run-time (milliseconds) that is opted for real-time classifications

Real-time Alarm Monitoring System for Detecting Driver Fatigue in Wireless Areas

The purpose of this paper was to develop a real-time alarm monitoring system that can detect the fatigue driving state through wireless communication. The drivers’ electroencephalogram (EEG) signals were recorded from occipital electrodes. Seven EEG rhythms with different frequency bands as gamma, hbeta, beta, sigma, alpha, theta and delta waves were extracted. They were simultaneously assessed using relative operating characteristic (ROC) curves and grey relational analysis to select one as the fatigue feature. The research results showed that the performance of theta wave was the best one. Therefore, theta wave was used as fatigue feature in the following alarm device. The real-time alarm monitoring system based on the result has been developed, once the threshold was settled by using the data of the first ten minutes driving period. The developed system can detect driver fatigue and give alarm to indicate the onset of fatigue automatically

Brain-Computer Interface Controlled Functional Electrical Stimulation System for Ankle Movement

Abstract Background Many neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI) is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals. Methods A noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot. Results Five able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77) with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions), and one subject had a single false alarm. Conclusions This study suggests that the integration of a noninvasive BCI with a lower-extremity FES system is feasible. With additional modifications, the proposed BCI-FES system may offer a novel and effective therapy in the neuro-rehabilitation of individuals with lower extremity paralysis due to neurological injuries