The effects of working memory on brain–computer interface performance

Abstract

Objective The purpose of the present study is to evaluate the relationship between working memory and BCI performance. Methods Participants took part in two separate sessions. The first session consisted of three computerized tasks. The LSWM was used to measure working memory, the TPVT was used to measure general intelligence, and the DCCS was used to measure executive function, specifically cognitive flexibility. The second session consisted of a P300-based BCI copy-spelling task. Results The results indicate that both working memory and general intelligence are significant predictors of BCI performance. Conclusions This suggests that working memory training could be used to improve performance on a BCI task. Significance Working memory training may help to reduce a portion of the individual differences that exist in BCI performance allowing for a wider range of users to successfully operate the BCI system as well as increase the BCI performance of current users.

Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training

“…EEG-based BCI strategies have been recently proposed as a promising stroke neurorehabilitation strategy to treat symptoms, including paralysis, cognitive disorders, and aphasia (19)(20)(21)(22)(23)(24)(25). Despite the large heterogeneity in the available literature, there is consensus that BCI-based training can help to improve UL motor function in stroke patients.…”

Examining sensory ability, feature matching and assessment-based adaptation for a brain–computer interface using the steady-state visually evoked potential

“…The procedures listed here for evaluating SSVEP-BCI performance can augment other evaluation parameters such as sufficient visual capacity, cognitive status, attention and working memory needed for BCI control (cf. [45,[50][51][52]). Overall, these results reiterate the need for comprehensive physical, sensory and neurological assessment when matching BCI systems to individuals who require AAC (e.g., [9,11]).…”

Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication

“…Aided AAC devices commonly impose substantial working memory demands (e.g., to locate the target icon and ignore distracting stimuli; Thistle & Wilkinson, 2013), and this is no different for BCI techniques. In addition, prior studies have shown that working memory skills (assessed via the List Sorting Working Memory Task; Gershon et al, 2013) are positively correlated with P300 speller performance in healthy adults (Sprague, McBee, & Sellers, 2016).…”