Neural network based auto association and time-series prediction for biosignal processing in brain-computer interfaces

In motor imagery (MI) based brain-computer interface (BCI), common spatial pattern (CSP) is most popularly used for discriminant feature extraction. However, the performance of CSP depends on the operational frequency bands, which are selected manually or set to a broad frequency range in most of the previously developed applications. Due to subject to subject or even trial to trial variability of frequency band affected by MI task, these methods suffer from poor performance. We have proposed a novel approach, using combination of multivariate iterative filtering (MIF) and CSP (MIFCSP), to automatically select optimal frequency bands based on MIF which can be further used for discriminant feature extraction. MIF decomposes the signal into several multivariate intrinsic mode functions, from which features are extracted using CSP. We select the minimum number of most significant features for which highest classification accuracy is achieved. Subsequently, linear discriminant analysis (LDA) classifier is used to classify different MI tasks. Experimental results for BCI competition IV dataset 2a and BCI competition III-IIIa are presented. For left hand versus right hand MI classification, proposed MIFCSP method provides 83.18% and 84.44% average accuracy, respectively. Superior classification performance confirms that MIFCSP is a promising candidate for MI BCI application.

Introduction: This current study is part of a larger, on-going clinical trial (NCT02098265), which evaluates non-invasive electroencephalographic (EEG) based brain-computer interface(BCI) therapy for restoration of distal upper extremity motor function in stroke survivors. We acquired data for 8 participants (mean age= 64 years) presenting with varying levels of upper-extremity motor deficits and chronicity since stroke. EEG based BCI task-related performance outcomes were compared with distal extremity behavioral testing on the 9-Hole Peg Test - 9HPT) to illustrate the relationship of BCI training performance on behavioral performance. Methods: EEG data is acquired with BCI2000, a 16 channel recording system (g.LADYbird-g. GAMMAsys-g. USBamp, Guger Technologies, Graz, Austria) with electrodes positioned according to the standard 10-20 system over the sensorimotor cortex at C3 & C4. Participants performed a hand movement task; randomly cued to move either their affected or unaffected hand depending on the appearance of virtual target and cursor on the screen. 9HPT Data was selected from two BCI conditions: 1) BCI with visual stimulus only, and 2) BCI with visual stimulation plus functional electrical stimulation (FES) of the impaired arm, and tongue stimulation (TS) at four time-points: baseline (prior to BCI therapy), mid-point of BCI therapy, post-therapy, and one month post-therapy. Results: BCI visual plus stimulus for the unaffected side was found to best relate to 9HPT scores (p= 0.0928) with a trend to significance. BCI visual plus stimulus for the affected side was not shown to correlate with 9HPT scores (p= 0.2655). The results suggest that 9HPT scores better relate with average task accuracy when the TS and FES adjuvants are incorporated (BCI stimulus vs 9H PT unaffected p= 0.09284, BCI stimulus vs 9H PT affected p= 0.26553) compared to the BCI visual only task (BCI visual vs 9HPT unaffected p=0.2702, BCI visual vs 9HPT affected (p= 0.89276). Conclusion: Non-invasive EEG-based BCI therapy may be suitable for the restoration of distal upper extremity motor function but average task accuracy does not appear to be a valid indicator of motor improvement. This finding suggests that visual only BCI systems are inadequate for motor rehabilitation.

F190. Mindfulness based stress reduction’s influence on brain-computer interface control

The Common Spatial Patterns (CSP) method is a widely used spatial filtering technique that can extract discriminative features for Electroencephalogram (EEG)-based brain computer interface (BCI) classification tasks. Since the EEG signal acquired on the scalp is a nonlinear composition of multiple signal and noise sources, in order to characterize the nonlinear data structure, nonlinear CSP methods have been proposed by using the kernel technique. Most kernel CSP methods calculate temporal covariance structure in a kernel feature space that leads to a large kernel matrix with each dimension equal to the number of time points multiplied by the number of classes. In this work, a kernel CSP method exploiting spatial covariance structure in the feature space is developed where the size of kernel matrix is the number of EEG channels, which is usually much less than that of time points. The proposed method was evaluated using motor imagery EEG data. Results indicate that the kernel CSP using spatial analysis can provide comparable performance to the existing methods using temporal analysis with less computational load.

One of the popular feature extraction algorithms for motor imagery (MI)-based brain-computer interface (BCI) is common spatial pattern (CSP). However, CSP is also very susceptive to the selection of the filter bands, the time windows, and the channels. In this paper, we proposed a novel regularized CSP (RCSP) method to optimize feature extraction in MI-BCI. Then, a robust classifier based on AdaBoost algorithm was presented to perform the classification of MI tasks. Finally, the framework was verified on two public BCI datasets (dataset 1 from the BCI Competition IV and dataset IVa from BCI Competition III). The results suggest the proposed approach achieved superior performance compared with classical CSP and other competing methods. Overall, this method not only improved classification performance, but also reduced the data requirements of other subjects.

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

This paper is focused on the experimental approach to explore the potential of independent component analysis (ICA) in the context of motor imagery (MI)-based brain-computer interface (BCI). We presented a simple and efficient algorithmic framework of ICA-based MI BCI (ICA-MIBCI) for the evaluation of four classical ICA algorithms (Infomax, FastICA, Jade, and Sobi) as well as a simplified Infomax (sInfomax). Two novel performance indexes, self-test accuracy and the number of invalid ICA filters, were employed to assess the performance of MIBCI based on different ICA variants. As a reference method, common spatial pattern (CSP), a commonly-used spatial filtering method, was employed for the comparative study between ICA-MIBCI and CSP-MIBCI. The experimental results showed that sInfomax-based spatial filters exhibited significantly better transferability in session to session and subject to subject transfer as compared to CSP-based spatial filters. The online experiment was also introduced to demonstrate the practicability and feasibility of sInfomax-based MIBCI. However, four classical ICA variants, especially FastICA, Jade, and Sobi, performed much worse as compared to sInfomax and CSP in terms of classification accuracy and stability. We consider that conventional ICA-based spatial filtering methods tend to be overfitting while applied to real-life electroencephalogram data. Nevertheless, the sInfomax-based experimental results indicate that ICA methods have a great space for improvement in the application of MIBCI. We believe that this paper could bring forth new ideas for the practical implementation of ICA-MIBCI.

Objective. Channel selection in the electroencephalogram (EEG)-based brain–computer interface (BCI) has been extensively studied for over two decades, with the goal being to select optimal subject-specific channels that can enhance the overall decoding efficacy of the BCI. With the emergence of deep learning (DL)-based BCI models, there arises a need for fresh perspectives and novel techniques to conduct channel selection. In this regard, subject-independent channel selection is relevant, since DL models trained using cross-subject data offer superior performance, and the impact of inherent inter-subject variability of EEG characteristics on subject-independent DL training is not yet fully understood. Approach. Here, we propose a novel methodology for implementing subject-independent channel selection in DL-based motor imagery (MI)-BCI, using layer-wise relevance propagation (LRP) and neural network pruning. Experiments were conducted using Deep ConvNet and 62-channel MI data from the Korea University EEG dataset. Main Results. Using our proposed methodology, we achieved a 61% reduction in the number of channels without any significant drop (p = 0.09) in subject-independent classification accuracy, due to the selection of highly relevant channels by LRP. LRP relevance-based channel selections provide significantly better accuracies compared to conventional weight-based selections while using less than 40% of the total number of channels, with differences in accuracies ranging from 5.96% to 1.72%. The performance of the adapted sparse-LRP model using only 16% of the total number of channels is similar to that of the adapted baseline model (p = 0.13). Furthermore, the accuracy of the adapted sparse-LRP model using only 35% of the total number of channels exceeded that of the adapted baseline model by 0.53% (p = 0.81). Analyses of channels chosen by LRP confirm the neurophysiological plausibility of selection, and emphasize the influence of motor, parietal, and occipital channels in MI-EEG classification. Significance. The proposed method addresses a traditional issue in EEG-BCI decoding, while being relevant and applicable to the latest developments in the field of BCI. We believe that our work brings forth an interesting and important application of model interpretability as a problem-solving technique.

The study aimed to compare the effects of combined action observation and motor imagery (AOMI) and motor imagery (MI)-based brain-computer interface (BCI) training on upper limb recovery, cortical excitation, and cognitive task performance in chronic stroke patients. 17 chronic stroke patients were recruited and randomly assigned to AOMI-based BCI (n = 9) and MI-based BCI groups (n = 8). The AOMI-based BCI group received AOMI-based BCI training via functional electrical stimulation (FES) feedback, whereas the MI-based BCI group obtained MI-based BCI training via FES feedback. Both groups participated in training for 12 sessions (3 days/week, consecutive four weeks). To evaluate upper limb function recovery, the Fugl–Meyer Assessment for upper extremity (FMA-UE) was employed. Event-related desynchronization (ERD) and online classification accuracy were utilized to measure cortical excitation of the affected sensorimotor hand region and cognitive task performance, respectively. Both AOMI and MI-based BCI training improved upper limb function in chronic stroke patients. However, the AOMI-based BCI group showed significantly greater motor gain than the MI-based BCI group. In addition, the AOMI-based BCI group demonstrated significantly greater cortical excitation of the affected sensorimotor hand region and cognitive task performance. The correlation analysis revealed that higher cognitive task performance during AOMI-based BCI training may promote greater cortical excitation of the affected sensorimotor hand region, which contributes to greater upper limb function improvement compared to MI-based BCI training.

Correlation-based channel selection and regularized feature optimization for MI-based BCI

Entropy, the complexity measures for time series, has found numerous successful applications in brain signal analysis such as detection of epileptic seizure and monitoring the depth of anesthesia. Renyi entropy generalizes the well-known Shannon entropy, and hence providing better flexibility in application to real data. The objective of this paper is to evaluate the effectiveness of Renyi entropy as feature extraction method for motor imagery (MI)-based brain–computer interface (BCI). In this paper, Renyi entropy has been implemented in MI systems of various settings using BCI competition data sets. The classification accuracy of Renyi entropy in all data sets is benchmarked against common spatial pattern (CSP), the state-of-the-art feature extraction method. For common binary class data sets, Renyi entropy achieves an accuracy of approximately 3.4 % higher for BCI Competition II Data Set III and 0.87 % lower for BCI Competition III Data Set I, and there is no difference in accuracy for BCI Competition III Data Set IVc when compared against conventional CSP. In small sample setting, the average classification accuracy using Renyi entropy is approximately 3.4 and 0.3 % higher as compared to conventional CSP and best performing variant of regularized CSP, respectively. In addition, Renyi entropy is also compared to other chaos-inspired feature extraction methods, namely Katz and Higuchi which are implemented for MI systems by earlier researchers. The effect of implementing Renyi entropy on multiple narrower frequency sub-bands is also investigated in this study. In multi-class setting, Renyi entropy shows no statistical difference (p = 0.6022, paired t test) in accuracy when compared to the algorithm of the BCI competition winner. However, Renyi entropy has an advantage of requiring only one single application of feature extraction regardless of the number of classes, while multiple implementation of CSP is required as CSP is originally designed for binary class problem. The successful application of Renyi entropy in all the aforementioned settings indicates that Renyi entropy is a viable feature extraction alternative for MI-based BCI systems.

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While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition.

This paper presents the major challenges and solutions for the Brain Computer Interfaces (BCIs), which are based on Steady State Visual Evoked Potentials (SSVEPs). A BCI utilizes the information transmitted by the brain; there are several methods available for analyzing these brain activities. One of the major challenges in BCI is to remove the noise successfully. Averaging along with Wavelets has been proposed in this paper for de-noising the brain activities. Moreover, three different approaches have been investigated to estimate the frequency spectrum of the brain signals. In addition to the well known Fourier Transform (FT) technique, for spectral estimation, there are many parametric and non parametric techniques for computing the frequency content of a signal. This paper presents a comparison between Fast Fourier Transform (FFT), MUltiple SIgnal Classification (MUSIC) and Linear Predictive Coding (LPC). The effectiveness of different techniques has been studied and the simulation results have shown that MUSIC outperforms the other approaches.

There remains an active investigation on elevating the classification accuracy and information transfer rate of brain-computer interfaces based on steady-state visual evoked potential. However, it has often been ignored that the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can be affected through the minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments involved 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the occipital area of the participants. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1–3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, EMSI outperformed MSI when comparing the ACA and RES values under the multichannel conditions. In conclusion, our results suggested that the use of multichannel configuration and employment of EMSI could make the performance of SSVEP-based BCIs more robust to the electrode shift from the optimal locations.