A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface

Abstract

Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environ ment through brain signals rather than through muscles, and restoring motor function by inducing activity-dependent brain plasticity. This paper presents a clinical study on the extent of detectable brain signals from a large population of stroke patients in using EEG-based motor imagery BCI. EEG data were collected from 54 stroke patients whereby finger tapping and motor imagery of the stroke-affected hand were performed by 8 and 46 patients, respectively. EEG data from 11 patients who gave further consent to perform motor imagery were also collected for second calibration and third independent test sessions conducted on separate days. Off-line accuracies of classifying the two classes of EEG from finger tapping or motor imagery of the stroke-affected hand versus the EEG from background rest were then assessed and compared to 16 healthy subjects. The mean off-line accuracy of detecting motor imagery by the 46 patients (mu=0.74) was significantly lower than finger tapping by 8 patients (mu=0.87, p=0.008), but not significantly lower than motor imagery by healthy subjects (mu=0.78, p=0.23). Six stroke patients performed motor imagery at chance level, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment (p=0.29). The off-line accuracies of the 11 patients in the second session (mu=0.76) were not significantly different from the first session (mu=0.72, p=0.16), or from the on-line accuracies of the third independent test session (mu=0.82, p=0.14). Hence this study showed that the majority of stroke patients could use EEG-based motor imagery BCI.

Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality

“…This self-regulation of brain activity during MI can be supported by providing visual or proprioceptive feedback about the current user's brain state (Boe et al, 2014;Dobkin, 2004;Gomez-Rodriguez et al, 2011) using brain-computer interfaces (BCIs) or brain-robot interfaces (BRIs), respectively (Birbaumer and Cohen, 2007;Fetz, 2007;Wolpaw, 2007). First studies applying these approaches in stroke rehabilitation are promising (Ang et al, 2011(Ang et al, , 2014Buch et al, 2008Buch et al, , 2012Gomez-Rodriguez et al, 2011;Prasad et al, 2010;Ramos-Murguialday et al, 2013;Shindo et al, 2011).…”

Brain–machine interfaces for rehabilitation of poststroke hemiplegia

“…Several single-case studies have reported improved motor function in the context of SMR-BMI applications (Broetz et al, 2010;Caria et al, 2011;Daly et al, 2009). Larger case-series documented recovery along various modalities, eg, reappearance of EMG signals in the paralyzed hand, decrease in resting threshold of MEPs elicited from the ipsilesional motor cortex, as well as improved clinical motor function scores (Ang et al, 2011;Prasad et al, 2010;Shindo et al, 2011). Further studies have suggested that ERD feedback coupled with somatosensory stimuli might be more effective for functional recovery than simple visual stimuli .…”

A novel hand strength assessment method integrated into haptic knob for stroke rehabilitation

“…This motivated the use of BrainComputer-Interface (BCI) to detect motor imagery with robotic feedback for stroke rehabilitation [10], [11], [12]. In this work, the Haptic Knob is used for the stroke patient to attempt to open or close fingers or rotate their forearms in order to help them acquire skills necessary for fine object manipulation.…”