Towards correlation-based time window selection method for motor imagery BCIs

Abstract

The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs.

Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs

“…This result of this is that only the largest coefficient is choosen. Conversely, for the HFCORRCA, we adopted a soft weighted function as formula (8) to combine all the correlation coefficients. We kept all coefficients, and combined them with different weights.…”

“…These results further confirm the justification for adopting a nonlinear weighted function to fuse the features. Note that the weighted function in formula (8) can be changed to other functions, such as w k = k −a + b(k = 1, 2, · · · , C) in formula (3). In future studies, we will investigate different weighting functions for different spatial filtering methods, which might further optimize system performance.…”

Unimanual Versus Bimanual Motor Imagery Classifiers for Assistive and Rehabilitative Brain Computer Interfaces

“…Further optimization of CSP features could be achieved by simultaneously or alternately optimizing temporal FIR and spatial filters [46][47][48]. Time window from which feedback is provided may also affect classification accuracy; methods such as correlation -based time window selection could be used to automatically detect optimal time window for each person [49].…”

Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI

“…When designing a BCI system, noninvasive EEG is the most employed brain imaging technique for extracting brain activity that codes the cognitive states and intentions of the user [4]. The brain control signals include event-related potential (ERP) [5]- [7], sensorimotor rhythm (SMR) [8]- [10], steady-state visual evoked potential (SSVEP) [11]- [13], hybrid BCI [14]- [16], and so forth. SSVEP-based BCI has received increasing interest from researchers because it requires less training of the user and has a relatively high information transfer rate (ITR) [17]- [25].…”