Steady-State movement related potentials for brain–computer interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain–computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data generating mechanism. The objective of this work is thus to examine the applicability of T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: i) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery (MI), and ii) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis (LDA), kernel Fisher discriminant (KFD) and support vector machines (SVMs) as well as a conventional type-1 FLS (T1FLS), simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification

Non-Linear Adapted Spatio-Temporal Filter for Single-Trial Identification of Movement-Related Cortical Potential

The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications

Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis

Motor imagery (MI) classification is one of the most widely-concern research topics in Electroencephalography (EEG)-based brain-computer interfaces (BCIs) with extensive industry value. The MI-EEG classifiers' tendency has changed fundamentally over the past twenty years, while classifiers' performance is gradually increasing. In particular, owing to the need for characterizing signals' non-Euclidean inherence, the first geometric deep learning (GDL) framework, Tensor-CSPNet, has recently emerged in the BCI study. In essence, Tensor-CSPNet is a deep learning-based classifier on the second-order statistics of EEGs. In contrast to the first-order statistics, using these second-order statistics is the classical treatment of EEG signals, and the discriminative information contained in these second-order statistics is adequate for MI-EEG classification. In this study, we present another GDL classifier for MI-EEG classification called Graph-CSPNet, using graph-based techniques to simultaneously characterize the EEG signals in both the time and frequency domains. It is realized from the perspective of the time-frequency analysis that profoundly influences signal processing and BCI studies. Contrary to Tensor-CSPNet, the architecture of Graph-CSPNet is further simplified with more flexibility to cope with variable time-frequency resolution for signal segmentation to capture the localized fluctuations. In the experiments, Graph-CSPNet is evaluated on subject-specific scenarios from two well-used MI-EEG datasets and produces near-optimal classification accuracies.Comment: 16 pages, 5 figures, 9 Tables; This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Steady-state movement related potentials for brain computer interfacing

An approach for brain computer interfacing (BCI) by analysis of the steady-state movement related potentials (ssMRP) is proposed in this paper. The neurological background of the ssMRPs which are primarily studied by means of the averaged electroencephalogram (EEG) signals are briefly reviewed. A simple feature extraction method is suggested for single trial ssMRP processing. The proposed BCI paradigm is tested by using the Fishers linear discriminant (FLD) classifier. The novelty of this approach is mainly in the application of rhythmic cues for BCI, simple recording setup, and straightforward computations which make the real-time implementations plausible

Brain signal analysis in space-time-frequency domain : an application to brain computer interfacing

In this dissertation, advanced methods for electroencephalogram (EEG) signal analysis in the space-time-frequency (STF) domain with applications to eye-blink (EB) artifact removal and brain computer interfacing (BCI) are developed. The two methods for EB artifact removal from EEGs are presented which respectively include the estimated spatial signatures of the EB artifacts into the signal extraction and the robust beamforming frameworks. In the developed signal extraction algorithm, the EB artifacts are extracted as uncorrelated signals from EEGs. The algorithm utilizes the spatial signatures of the EB artifacts as priori knowledge in the signal extraction stage. The spatial distributions are identified using the STF model of EEGs. In the robust beamforming approach, first a novel space-time-frequency/time-segment (STF-TS) model for EEGs is introduced. The estimated spatial signatures of the EBs are then taken into account in order to restore the artifact contaminated EEG measurements. Both algorithms are evaluated by using the simulated and real EEGs and shown to produce comparable results to that of conventional approaches. Finally, an effective paradigm for BCI is introduced. In this approach prior physiological knowledge of spectrally band limited steady-state movement related potentials is exploited. The results consolidate the method.EThOS - Electronic Theses Online ServiceGBUnited Kingdo