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Table 6 Exemplary features and specifications of future BCI-hand robot systems

From: Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

Component

Features and specifications

Data acquisition system and software

Dry EEG system with 8–16 channels, comfortable and easy to use

Inclusion of other bio-signal sensors such as EMG, EOG, force, accelerometers to remove artifacts and improve classification

Robust and reliable signal processing software: machine learning-based algorithms that discriminate brain states such as MI or evoked potentials with high classification accuracies (≥ 95%) and lower calibration times

Hand Robot

Safe, comfortable and aligned with the hand’s range of motion

Effective in providing kinaesthetic feedback

Use of back-drivable or soft actuators that effectively assist movement without additional injury

Multiple levels of safety and emergency features (mechanical, electronic, software), clear and obvious operation

Visual cue and feedback

Provide rich visual cue and feedback to intended tasks, geometric representation of the hand (video or simulated environment), can be in multiple platforms such as display monitors or VR/AR on a head-mounted device

Gamification of therapy exercises to provide an engaging regime to stroke patients