Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives

By Han Yuan, Bin He. IEEE Transactions on Biomedical Engineering, 2014

454 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/1eac72e897b5794c150ec50d1c352eca50cd48b6

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

A comprehensive review of EEG-based brain–computer interface paradigms (2019, unknown): “The sensorimotor rhythms paradigm is one of the most popular motor imagery paradigms (e.g. [34, 35]).”
Quo vadis artificial intelligence? (2022, background): “Additional advancements in the study of brain functions and the development of nanomaterial and implant surgery techniques are necessary for implementations on a broader scale [95, 96].”
EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks (2015, background): “such as the P300 [4]–[6], steady-state visual evoked potentials [7]–[9], slow cortical potentials [10], and sensorimotor rhythms (SMRs) [11], [12].”
EEG-Based Brain-Computer Interfaces (2017, methodology): “Subjects can learn to use SMR amplitudes to control movement in multiple dimensions simultaneously and to support sequential mouse-like control [14 e 16].”
A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines (2017, background): “2601240 restore regular functions for people with severe neuromuscular disabilities or enhance certain functions for healthy person via a new signal pathway [2].”
Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review (2024, background): “It is widely adopted in brain–computer interface (BCI) research, where it enables the control of machines through imagery or voluntary modulation of movement [112].”