Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Summary

This study introduces a brain-computer interface (BCI) system for stroke patients, which combines electroencephalography and electrooculography signals to control an upper limb robotic hand, enhancing daily activities. The evaluation used the Berlin Bimanual Test for Stroke (BeBiTS).

Abstract

This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in everyday tasks while interacting with a robotic hand. We evaluated the effectiveness of this BCI-robot system using the Berlin Bimanual Test for Stroke (BeBiTS), a set of 10 daily living tasks involving both hands. Eight stroke patients participated in this study, but only four participants could adapt to the BCI robot system training and perform the postBeBiTS. Notably, when the preBeBiTS score for each item was four or less, the BCI robot system showed greater assistive effectiveness in the postBeBiTS assessment. Furthermore, our current robotic hand does not assist with arm and wrist functions, limiting its use in tasks requiring complex hand movements. More participants are required to confirm the training effectiveness of the BCI-robot system, and future research should consider using robots that can assist with a broader range of upper limb functions. This study aimed to determine the BCI-robot system's ability to assist patients in performing daily living activities.

Introduction

Impairment of upper extremity function due to stroke limits the ability to perform daily activities, especially bimanual tasks¹. Hand rehabilitation is, therefore, a key component of stroke rehabilitation, with mirror therapy² and Constraint-Induced Movement Therapy (CIMT)³ being well-known approaches. Recent research indicates that EEG-based Brain-Computer Interface (BCI) robot systems can be an effective assistive therapy for improving hand function recovery in stroke patients^4,5,6. BCI robotic systems ....

Protocol

The Seoul National University Bundang Hospital Institutional Review Board reviewed and approved all experimental procedures (IRB No. B-2205-756-003). We recruited eight stroke patients and thoroughly explained the relevant details before obtaining their consent. After obtaining informed consent, the protocol proceeds as follows: we perform a BeBiTS assessment before BCI training, followed by BCI training using EOG and EEG. Afterward, participants wear the robot to perform another BeBiTS assessment (Figure 1).

1. BCI-robot training system setup

1. Patient recruitment
   1. Perform....

Results

Figure 12 shows the results of EOG and EEG training. Figure 12A represents the results of a well-trained participant. The EOG training values are consistent, with the average (orange bold line) properly reaching the threshold line. The EEG training results also clearly distinguish between the blue (resting state) and the red (motor imagery) lines.

In contrast, Figure 12B shows the results of a participant.......

Discussion

This research presented a BCI upper limb assistive robotic system to support stroke patients in executing daily tasks. We assessed the efficacy of bimanual tasks through the BeBiTS test¹⁵ and implemented training for the operation of the upper limb assistive robot via the BCI system¹⁴. This approach, in contrast to conventional rehabilitation procedures, allows patients to actively engage in their recovery by controlling the robot's operations according to their intenti.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BCI2000	open-source		general-purpose software system for brain-computer interface (BCI) research that is free for non-commercial use
BrainVision LSL Viewer	Brain Products GmbH		a handy tool to monitor its LSL EEG and marker streams.
eego mini amplifier with 8-channel (F3, F4, C3, Cz, C4, P3, P4, EOG) waveguard original caps	Ant Neuro, Netherlands		Compact and lightweight design: The eego mini amplifier is small and lightweight, offering excellent portability and suitability for EEG recording in various environments.
Neomano	neofect, Korea		Glove Material: Leather, velcro, Non-slip cloth Wire Material: Synthetic Thread Weight: 65 g (without batt.) cover three fingers: the thumb, index, and middle fingers
personal computer (PC) with custom BCI software			window laptop