Exploration of User's Mental State Changes during Performing Brain-Computer Interface.

Li-Wei Ko,Wen-Chieh Lin,Yi-Chieh Lee,Rupesh Kumar Chikara

doi:10.3390/s20113169

Abstract

Substantial developments have been established in the past few years for enhancing the performance of brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user’s mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user’s visual area. BCI user’s cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users’ physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user’s cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1–4 Hz), theta (4–7 Hz), and beta (13–30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1–4 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user’s cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.

Highlights

The brain–computer interface (BCI) is a promising communication pathway that translates brain signals into a command to control a device [1]
Previous study reported that the frontal and occipital cortices were selected as a region of interest because the frontal lobe has been associated with the executive function, working memory, mental focus state, attention, and lost-in-thought state [50], and the occipital lobe has been linked to visual stimulation and state visually evoked potentials (SSVEP)-BCI task [13,23,51]
Results from S5, S8, and S13 show that changes in the neurophysiological states of the individual subject affect the performance of the SSVEP-BCI system

Summary

Introduction

The brain–computer interface (BCI) is a promising communication pathway that translates brain signals into a command to control a device [1]. Despite the significant development of BCI system, there are still some problems in current BCI technologies, like how to recognize that the cognitive state of BCI user is mental focus or lost-in-thought state. These cognitive states changes affect the performance of BCI technologies, like robotic arm for stroke patients, wheelchair and drone. The SSVEP elicited by 12 Hz or 30 Hz flashing frequencies, which revealed neural activities changes over occipital lobe of the brain under attention state (mental focus) [5]

Methods

Results

Discussion

Conclusion