» Articles » PMID: 9749678

Answering Questions with an Electroencephalogram-based Brain-computer Interface

Overview
Date 1998 Sep 28
PMID 9749678
Citations 15
Authors
Affiliations
Soon will be listed here.
Abstract

Objective: To demonstrate that humans can learn to control selected electroencephalographic components and use that control to answer simple questions.

Methods: Four adults (one with amyotrophic lateral sclerosis) learned to use electroencephalogram (EEG) mu rhythm (8 to 12Hz) or beta rhythm (18 to 25Hz) activity over sensorimotor cortex to control vertical cursor movement to targets at the top or bottom edge of a video screen. In subsequent sessions, the targets were replaced with the words YES and NO, and individuals used the cursor to answer spoken YES/NO questions from single- or multiple-topic question sets. They confirmed their answers through the response verification (RV) procedure, in which the word positions were switched and the question was answered again.

Results: For 5 consecutive sessions after initial question training, individuals were asked an average of 4.0 to 4.6 questions per minute; 64% to 87% of their answers were confirmed by the RV procedure and 93% to 99% of these answers were correct. Performances for single- and multiple-topic question sets did not differ significantly.

Conclusions: The results indicate that (1) EEG-based cursor control can be used to answer simple questions with a high degree of accuracy, (2) attention to auditory queries and formulation of answers does not interfere with EEG-based cursor control, (3) question complexity (at least as represented by single versus multiple-topic question sets) does not noticeably affect performance, and (4) the RV procedure improves accuracy as expected. Several options for increasing the speed of communication appear promising. An EEG-based brain-computer interface could provide a new communication and control modality for people with severe motor disabilities.

Citing Articles

EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review.

Cioffi E, Hutber A, Molloy R, Murden S, Yurkewich A, Kirton A Clin Neurophysiol. 2024; 167:143-166.

PMID: 39321571 PMC: 11845253. DOI: 10.1016/j.clinph.2024.08.009.


Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

Kawala-Sterniuk A, Browarska N, Al-Bakri A, Pelc M, Zygarlicki J, Sidikova M Brain Sci. 2021; 11(1).

PMID: 33401571 PMC: 7824107. DOI: 10.3390/brainsci11010043.


Electrophysiological responses of relatedness to consecutive word stimuli in relation to an actively recollected target word.

Dijkstra K, Farquhar J, Desain P Sci Rep. 2019; 9(1):14514.

PMID: 31601871 PMC: 6786994. DOI: 10.1038/s41598-019-51011-4.


Covert Intention to Answer to Self-Referential Questions Is Represented in Alpha-Band Local and Interregional Neural Synchronies.

Choi J, Cha K, Kim K Comput Intell Neurosci. 2019; 2019:7084186.

PMID: 30723496 PMC: 6339759. DOI: 10.1155/2019/7084186.


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

Pitt K, Brumberg J Am J Speech Lang Pathol. 2018; 27(3):950-964.

PMID: 29860376 PMC: 6195025. DOI: 10.1044/2018_AJSLP-17-0135.