Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives

Han Yuan, Bin He, Han Yuan, Bin He

Abstract

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e., the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g., electroencephalography (EEG), and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR-BCIs and future outlooks are also discussed.

Figures

Fig. 1
Fig. 1
A schematic diagram of the essential components of a Brain-Computer Interface system.
Fig. 2
Fig. 2
Time, frequen cy, and spatial characteristics of sensorimotor rhythms. (a) Steps of feature extraction for sensorimotor rhythms [33]. It is difficult to detect a coherent component in the raw EEG signal depicted in the top frame because there is a lot of noise in the signal. The second frame shows the signal after being processed through a surface Laplacian filter that focuses on EEG components in a specific spatial frequency range. As shown in the third frame, the signal is then band-pass filtered to isolate the frequencies of interest. The features become evident in the fourth frame as they are extracted by using a grand averaging method over a fixed bin or window size (b) An example of the time-frequency representation of SMR dynamics [33]. (c) Source localization for decreases of alpha (blue ball) and beta rhythms (green ball) induced by motor imagery of right hand, co-localized with BOLD fMRI activations (white arrow) [39]. Figures are adapted with permission.
Fig. 3
Fig. 3
Examples of SMR-based BCI that can achieve 1D (a), 2D (b), 3D (c) control of cursor movement, or navigate in 3D space (d), adapted from [16,19] with permission.

Source: PubMed

Připravované klinické studie

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