Abstract
Action observation (AO)-based brain-computer interface (BCI) is an important technology in stroke rehabilitation training. It has the advantage of simultaneously inducing steady-state motion visual evoked potential (SSMVEP) and activating sensorimotor rhythm. Moreover, SSMVEP could be utilized to perform classification. However, SSMVEP is composed of complex modulation frequencies. Traditional canonical correlation analysis (CCA) suffers from poor recognition performance in identifying those modulation frequencies at short stimulus duration. To address this issue, task-related component analysis (TRCA) was utilized to deal with SSMVEP for the first time. An interesting phenomenon was found: different modulated frequencies in SSMVEP distributed in different task-related components. On this basis, a multi-component TRCA method was proposed. All the significant task-related components were utilized to construct multiple spatial filters to enhance the detection of SSMVEP. Further, a combination of TRCA and CCA was proposed to utilize both advantages. Results showed that the accuracies using the proposed methods were significant higher than that using CCA at all window lengths and significantly higher than that using ensemble-TRCA at short window lengths (≤2 s). Therefore, the proposed methods further validate the induced modulation frequencies and will speed up the application of the AO-based BCI in rehabilitation.
Highlights
The results showed that observing the designed gaiting stimulus could simultaneously induce steady-state motion visual evoked potential (SSMVEP) in the occipital area and activate sensorimotor rhythm (SMR) in the primary sensorimotor area
The results indicate that (1) the proposed task-related component analysis (TRCA)-based methods are capable of automatically detecting several significant task-related components from the EEG data induced by the gaiting stimulus, and (2) provide a significant advantage with short duration of stimulation (≤2 s)
These results indicated that the SSMVEP induced by the gaiting stimulus appeared consistently and robustly in every task block and further proved the validity of the modulated frequencies induced by the gaiting stimulus
Summary
Action observation (AO), which can evoke the mirror neuron system (MNS), is an alternative approach for stroke rehabilitation [1]. While AO provides involuntary sensory stimulation for patients, the effect on the brain plasticity is limited. Sensory stimulation with the user’s own volitions can further promote brain plasticity [4]. Brain-computer interface (BCI) is a novel method that can obtain individuals’ volitions to interact with external environments without using regular peripheral nerves and muscles [5]. It reveals great potentials for enhancing brain plasticity in rehabilitation, such as motor imagery (MI)-based BCI [6]
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