Abstract
Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.
Highlights
To help patients with functional motor disabilities, such as amyotrophic lateral sclerosis (ALS) or spinal cord injury (SCI), Brain-Computer Interface (BCI) has been researched for over decades
The aim of this paper is to: (i) propose a novel framework for motor imagery tasks based on spatial filters, automatic feature selection, and traditional classifier; (ii) evaluate the proposed methods on both a self-acquired dataset and a BCI Competition dataset in terms of accuracy, F1-score, and Receiver operator character (ROC)
Examine how well linear discriminative analysis (LDA) adapts to the self-acquired dataset and evaluate its perTo examine how well adapts to the self-acquired dataset andvs
Summary
To help patients with functional motor disabilities, such as amyotrophic lateral sclerosis (ALS) or spinal cord injury (SCI), Brain-Computer Interface (BCI) has been researched for over decades. Various applications applying BCI to real-world problems have been carried out: brain-actuated speller where a patient could type words on a computer screen with their thought [2,3]; controlling computer cursor by gazing [4]; controlling incoming calls [5]; computer control interface [6]. Event-related desynchronization/synchronization (ERD/ERS), in which the amplitude of mu and beta rhythms is suppressed or increased depending on the event [8,9,10], is one of the main types of non-invasive BCI. MI has the advantage over other methods as it only requires mental simulation of movements without actual actions or external stimuli
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