Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects

Abstract

Brain-computer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development have grown explosively over the past two decades. Efforts have recently begun to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this review, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology, and identify potential users and potential applications. Finally, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.

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(a) EEG-based BCI systems: i) Sensorimotor rhythm (SMR) BCI [83, 109]. EEG activity is recorded over the sensorimotor cortex. Users are trained to control the amplitude of the μ rhythm (8-12 Hz) or the β rhythm (18-26 Hz) in order to move a computer cursor to a top target or a bottom target on a computer screen. Frequency spectra for vertical cursor movement (top or bottom target) indicate that the user's control focuses in the μ-rhythm frequency band. Sample EEG traces (bottom) show that the μ rhythm is prominent with top targets and minimal with bottom targets. An SMR BCI can provide two or even three-dimensional movement control. (Adapted from Wolpaw JR et al [136], with permission from the Institute of Electrical and Electronics Engineers); ii) P300 event-related potential BCI [6, 19]. A matrix of possible selections is shown on a computer screen. EEG activity is recorded over the centroparietal cortex while these selections flash in succession. Only the selection desired by the user elicits a P300 potential (e.g., a positive voltage deflection about 300ms after the flash). (Adapted from Donchin E et al [11], with permission from the Institute of Electrical and Electronics Engineers); (b) ECoG-based BCI systems: Sample topographies of vertical and horizontal control using ECoG signals. These topographies show the color-coded correlation (i.e., r² values) of the cortical activity with vertical or horizontal movement. The level of task-related control of different cortical areas is indicated. Actual and imagined tongue movements were used for vertical control, while actual and imagined hand movements were used for horizontal control. The traces below each topography show r² values for the locations (stars) used online. The frequency bands used online are indicated by yellow bars. Actual and imagined tasks presented similar activity patterns over locations active with motor and motor imagery tasks. (Adapted from Schalk G et al [20], with permission from the Institute of Physics Publishing); (c) Intracortical-based BCI systems: Top left panel - An example of a 100-microelectrode array for chronic implantation in human motor cortex to record neuronal action potentials and/or local field potentials. Top right panel - Placement of an electrode array in the human motor cortex (arrow). (Adapted from Hochberg LR et al [18], with permission from Macmillan Publisher Ltd.) Bottom panel - three-dimensional cursor movements by groups of individual neurons in the motor cortex of a monkey; (left) average correlation of the firing rate of a single cortical neuron with target direction over daily training sessions; (right) resulting improvement in BCI performance, measured as the mean target radius required to maintain a 70% target hit rate. The size of the target needed decreased as the correlations of the firing rates of the neurons controlling cursor movement with the target direction increased. (Reproduced from Taylor DM et al [13], with permission from the American Association for the Advancement of Science.)

2

Essential elements and operation of a BCI system (modified from Wolpaw JR et al [14], and Leuthardt EC et al [36], with permission from Elsevier and Wolters Kluwer respectively). Brain signals that carry the intent of the user are first acquired by electrodes placed on the scalp (EEG), beneath the skull and over the cortical surface (ECoG), or within brain tissue (intracortical). These brain signals are digitized, and specific signal features are extracted. The extracted signal features are translated into device commands that activate and control assistive technology used for: communication (e.g., spelling on a computer screen); movement control (e.g., robotic arm) (Credit: Copyright Fraunhofer IPA)); environmental control (e.g., TV, light, temperature, etc); locomotion (e.g., electric wheelchair); or neurorehabilitation (adapted from Daly JJ et al [129], with permission from the Journal of Rehabilitation Research and Development).