Recognizing Motor Imagery Between Hand and Forearm in the Same Limb in a Hybrid Brain Computer Interface Paradigm: An Online Study

Abstract

Brain computer interfaces (BCIs) based on motor imagery (MI) play an important role in helping to improve and restore the loss of physical function. However, traditional MI-BCIs are limited to the motion intention of gross limb, which places many restrictions on their applications. This study proposes a hybrid paradigm based on MI and the steady-state somatosensory evoked potential, with the aim of improving the spatial resolution of MI recognition. Twelve subjects participated in this study. They performed MI tasks under MI and hybrid conditions. In the MI condition, subjects only performed MI tasks, whereas, in the hybrid condition, they received an electrical stimulus while performing the same tasks. Under the hybrid condition, subjects were required not to pay extra attention to the electrical stimulation. The MI task included imagining clenching the right hand and lifting the right forearm. The classifier was built using the filter bank common spatial pattern algorithm and a support vector machine, and online experiments were used to verify the recognition of two MI tasks. During the online experiments, all subjects were able to output different commands at a recognition accuracy far higher than the random level. The average classification accuracy of the hybrid condition reached 76.39%, with a maximum value of 88.34%, which was about 11% higher than that of the MI condition. Moreover, based on offline data, the classification performance using the event-related desynchronization (ERD) feature under the hybrid condition did not differ significantly from that under the MI condition, indicating that the introduction of electrical stimulation did not interfere in the separability of ERD. The proposed paradigm improved the efficiency of decoding multiple MI locations within a single limb. Despite the introduction of external stimuli, users could still drive the new system in the same way as MI in traditional MI-BCI. INDEX TERMS Motor imagery, event-related desynchronization (ERD), steady-state somatosensory evoked potential (SSSEP), hybrid brain-computer interface.

A hybrid brain-computer interface for closed-loop position control of a robot arm

“…In [39], the authors considered both amplitude and phase of the EEG signals to obtain more reliable CSP features, responsible for improving the classification accuracy for the 2-class classification problem. In their formulation, the objective function appears similar to (8) with 1 C and 2 C replaced by * 1 C and *…”

“…BCI technology captures the human motorintention to translate the thoughts into commands and actuates the robot to execute a mentally planned complex task. A BCI framework provides a non-muscular channel of communication with the outer world to enhance the quality of life of people suffering from brainstem stroke, neuro-muscular Hybrid BCI [1] is a widely used name in the BCI technology. Generally, it refers to multiple modalities of acquisition of brain activities, including functional Near Infrared Spectroscopy (fNIRS), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Electro-Corticography (E-Cog), and the like.…”

Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery

“…Previous studies have found that MI could induce event-related desynchronization (ERD) in a similar fashion to ME. The characteristics of ERD are an energy decrease in alpha (8)(9)(10)(11)(12)(13) Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) bands [12], [13]. Different imaginary tasks will induce different neural activities in sensorimotor areas.…”

“…Studies have shown that SSSEP induced by somatosensory stimulation, which is applied to the target limb at a specific frequency, could be influenced by the same limb MI, thus forming a new EEG feature associated with motor consciousness [23]. MI decoding using the MI-SSSEP feature has shown many advantages, such as significantly improving the recognition rate of left/right hand MI [24] and increasing the recognition rate of adjacent joints in unilateral limb [25]. This feature comes from two neural activities: motor intention and somatosensory response to the same limb, which theoretically should have better task specificity, and thus, could be better distinguished from other mental tasks.…”

Single-Trial Classification of Different Movements on One Arm Based on ERD/ERS and Corticomuscular Coherence

“…As a direct means of communication combining human brain with the external devices, the electroencephalography (EEG)-based brain-machine interface (BMI) can be considered as being the main way of communication for people affected by motor disabilities [1]- [3]. Some external devices such as wheel chair, robotic arm and neuroprosthetics, are controlled by BMI to restore motor function [4]- [8]. Bypassing the conventional neural muscular conduction pathway, human motion intentions can be decoded directly into machinery commands through BMI [9].…”