A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots.

TLDR: The results show that subjects can rapidly master the authors' asynchronous EEG-based BCI to control a wheelchair and can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities.

About: This article is published in Clinical Neurophysiology.The article was published on 2008-09-01 and is currently open access. It has received 644 citations till now. The article focuses on the topics: Wheelchair.

Figures

Fig. 6. Top view of the random paths in Task 2. Trial 1 placed in upper row, first column. Trial 10 placed in second row, last column.

Fig. 7. Top view of the world and the path stretches. Stretches F1 and F2 were labelled as Forward, R1 and R2 labelled as Right, L labelled as Left, and SD1 and SD2 labelled as strategy dependent. The subjects can go through SD1 by means of two strategies, either executing Forward or executing Right followed by Left. Through SD2, subjects can execute either Forward or Left followed by Right.

Table 1 LDA train-test classification accuracies on the configuration

Fig. 1. (Left) Monitor display in a first person view from the Start. The white cursor at the center is the fixation point. The rectangle at the bottom is the simulated wheelchair. (Right) Top view of the simulated world and the pre-specified path.

Fig. 8. Percentage of reached final goals over sessions. The time elapsed between sessions was: 1 day between sessions 1 and 2, 2 months between sessions 2 and 3, 1 h between sessions 3 and 4, and 1 day between sessions 4 and 5.

Fig. 2. Electrode discrimination index values De (see Section 2.3.2) for the selected frequencies for each subject, and the associated scalp distribution of the averaged logarithmic transform of the power spectral density, Log(PSDe), for each class. For subject 1, De is higher at left temporal, central and right occipital areas. For subject 2, at 10 Hz it is higher at right centro-parietal areas, and at 12 and 14 Hz it is higher at bilateral parietal areas. These areas correspond with those where the differences between the averaged Log(PSDe) patterns associated to each mental task is the biggest.