Abstract
With the increasing development of brain imaging and sensing technologies, a wide variety of medical signals in multiple modalities have facilitated a better understanding of mental health. The brain-computer interface (BCI) is a technology capable of detecting mental states automatically by employing various brain sensing and machine learning methods. This contributes to efforts involving neurological disease management and restoration of cognitive function. In this study, we present an independent decision path fusion (IDPF) method by developing a bimodal asynchronous BCI based on electroencephalographs (EEGs) and functional near-infrared spectroscopy (fNIRS) to discriminate multiple mental states. The proposed IDPF method generates several independent decision paths, each of which is capable of analyzing cerebral information with respect to a specific aspect, thus interpreting the brain state from multiple points of view. Moreover, in one particular decision path for the fNIRS analysis of the IDPF method, we modified the EEG-based common spatial pattern (CSP) algorithm according to the characteristics of fNIRS. The results show that the modified common spatial pattern (MCSP) significantly outperforms CSP in fNIRS-based BCIs. Through validation on an open-access EEG-fNIRS dataset and comparison with recent studies, we found that our IDPF method achieves a high accuracy of 70.32% for a four-class classification problem (left hand motor imagery, right hand motor imagery, mental arithmetic, and resting state).
Highlights
Brain–computer interface (BCI) is used extensively in interpreting different mental states, which can trigger alerts automatically when detecting abnormal brain activities in patients with mental illnesses, and assist paralyzed patients to control external equipment without voluntary muscular intervention
BCI technology is practiced in neurological rehabilitation, where it is applied to help restore motor or cognition functions of patients, The associate editor coordinating the review of this manuscript and approving it for publication was Jenny Mahoney
Due to the relatively small size of the dataset, to make the best use of the data, we applied leave-one-out cross-validation strategy to evaluate the proposed independent decision path fusion (IDPF) method in this research
Summary
Brain–computer interface (BCI) is used extensively in interpreting different mental states, which can trigger alerts automatically when detecting abnormal brain activities in patients with mental illnesses, and assist paralyzed patients to control external equipment without voluntary muscular intervention. Through imaginary of left or right hand movement, the amputees can control the movement of corresponding artificial prosthesis as long as their mental states can be accurately detected. This powerful tool is nowadays closely associated with numerous healthcare related applications [1]–[6]. BCI technology is practiced in neurological rehabilitation, where it is applied to help restore motor or cognition functions of patients, The associate editor coordinating the review of this manuscript and approving it for publication was Jenny Mahoney. In motor neuron disease (MND) patients, BCI-based wheelchairs [4] and robotic arms [5] are expected to provide convenience in daily life. The BCI for automated task load monitoring in robotic surgery was developed [6]
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