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Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface

Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface

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Nonstationary learning refers to the process that can learn patterns from data, adapt to shifts, and improve performance of the system with its experience while operating in the nonstationary environments (NSEs). Covariate shift (CS) presents a major challenge during data processing within NSEs wherein the input-data distribution shifts during transitioning from training to testing phase. CS is one of the fundamental issues in electroencephalogram (EEG)-based brain-computer interface (BCI) systems and can be often observed during multiple trials of EEG data recorded over different sessions. Thus, conventional learning algorithms struggle to accommodate these CSs in streaming EEG data resulting in low performance (in terms of classification accuracy) of motor imagery (MI)-related BCI systems. This chapter aims to introduce a novel framework for nonstationary adaptation in MI-related BCI system based on CS detection applied to the temporal and spatial filtered features extracted from raw EEG signals. The chapter collectively provides an efficient method for accounting nonstationarity in EEG data during learning in NSEs.

Chapter Contents:

  • Abstract
  • 7.1 Introduction
  • 7.2 Background
  • 7.2.1 Covariate shift in EEG signals
  • 7.2.2 Adaptive learning methods in EEG-based BCI
  • 7.3 Covariate shift detection-based nonstationary adaptation (CSD-NSA) algorithm
  • 7.3.1 Problem formulation
  • 7.3.2 Covariate shift detection (CSD) test
  • 7.3.3 Supervised CSD–NSA algorithm
  • 7.3.4 Unsupervised CSD-NSA algorithm
  • 7.3.4.1 Probabilistic k-nearest neighbor
  • 7.4 Experimental validation of the CSD-NSA algorithms
  • 7.4.1 EEG dataset
  • 7.4.2 Signal processing and feature extraction
  • 7.4.3 Feature selection and parameter estimation
  • 7.4.4 Empirical results
  • 7.5 Discussion and future prospects
  • References

Inspec keywords: spatial filters; medical signal processing; signal classification; learning (artificial intelligence); brain-computer interfaces; electroencephalography; feature extraction

Other keywords: CS detection; covariate shift detection-based nonstationary adaptation; low performance; nonstationary learning; input-data distribution shifts; conventional learning algorithms struggle; MI-related BCI system; EEG data; data processing; NSEs; nonstationary environments; motor-imagery-based brain-computer interface; electroencephalogram-based brain-computer interface systems; motor imagery-related

Subjects: Electrodiagnostics and other electrical measurement techniques; Biology and medical computing; Data handling techniques; User interfaces; Electrical activity in neurophysiological processes; Knowledge engineering techniques; Other topics in statistics; Filtering methods in signal processing; Bioelectric signals; Digital signal processing

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