Latent variable method for automatic adaptation to background states in motor imagery BCI

Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives

Introduction

Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal.

Methods

To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives.

Results

Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks.

Discussion

All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

The effects of visual distractors on cognitive load in a motor imagery brain-computer interface

A brain-computer interface (BCI) is a system that translates neural activity into a practical output. Its functionality, therefore, depends not only on the computer itself, but also on the cognitive system of the user. Distractors have the potential to capture attention, increase cognitive load, and may therefore impact BCI use. The purpose of the current study is to determine the effects of small visual distractors on the cognitive load of users of a motor imagery-BCI, and to examine whether these distractor-mediated effects can be improved by modifying the task interface. Sixteen typically-developed participants completed two sessions of online motor imagery to control an EEG-BCI, under conditions of no distractors, visual distractors, and cognitive strategies (intended to mitigate cognitive load) amid distractors. Cognitive load for each session was assessed through both a ratio of theta to alpha power and the NASA-Task Load Index (NASA-TLX). Task-irrelevant visual stimuli were found to significantly increase the objective measure of cognitive load, particularly for parietal channels. Subjective cognitive load as indexed by the NASA-TLX was predictive of a decrease in BCI performance for participants with below 0.75 classification accuracy (R² = 0.32, p < 0.001), which may indicate a differential susceptibility to changes in workload for "low"-performing participants. Quantifying and addressing the increased cognitive load imparted by distractors on BCI users can aid in the future applicability of the technology in real-world settings.

Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback-Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.