What Internal Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?

Abstract

Description In this review article, we aimed to create a summary of the effects of internal variables on the performance of sensorimotor rhythm-based brain computer interfaces (SMR-BCIs). SMR-BCIs can be potentially used for interfacing between the brain and devices, bypassing usual central nervous system output, such as muscle activity. The careful consideration of internal factors, affecting SMR-BCI performance, can maximize BCI application in both healthy and disabled people. Internal variables may be generalized as descriptors of the processes mainly dependent on the BCI user and/or originating within the user. The current review aimed to critically evaluate and summarize the currently accumulated body of knowledge regarding the effect of internal variables on SMR-BCI performance. The examples of such internal variables include motor imagery, hand coordination, attention, motivation, quality of life, mood and neurophysiological signals other than SMR. We will conclude our review with the discussion about the future developments regarding the research on the effects of internal variables on SMR-BCI performance. The end-goal of this review paper is to provide current BCI users and researchers with the reference guide that can help them optimize the SMR-BCI performance by accounting for possible influences of various internal factors.

Keywords: BCI accuracy; BCI adoption rates; BCI literacy; BCI performance; amyotrophic lateral sclerosis (ALS); attention; brain-computer interfaces (BCIs); depression; distraction; electroencephalography (EEG); event-related desynchronization (ERD); information transfer rate (ITR); internal variables; mental state; mood; motivation; motor imagery; neuroprosthetics; psychological variables; quality of life (QoL); sensorimotor rhythm (SMR); signal classification accuracy.

Figure 1

Recording of magnetic (MEG) and electric (EEG, ECoG) brain activity that can be used for Brain-Computer Interface (BCI) applications. Left: Example of magnetoencephalography (MEG) at BioMag Laboratory, Helsinki University Central Hospital; Middle: Example of electroencephalography (EEG) at the Department of Biophysics, Vilnius University; Right: Example of electrocorticography (ECoG) at the Comprehensive Epilepsy Surgery Center, AdventHealth Orlando. (Photographs courtesy of the authors.)

Figure 2

Brain Computer Interface (BCI) system set-up. A task (for example, imagining closing and opening the hand) triggers specific brain activity within the BCI user (for example, event-related desynchronization) that is detected by EEG, MEG, or ECoG. BCI processes this acquired signal, extracting relevant features according to a predefined or “adaptive” algorithm. The BCI translates the detected features into a device command (for example, a forward wheelchair movement). Device commands commonly involve directional control.

Figure 3

Examples of motor-imagery related responses during the motor-imagery task (upper left), recorded with different imaging modalities. Motor imagery is defined as “a mentally rehearsed task in which movement is imagined but not performed.” Motor imagery tasks may include practicing making a fist, walking, or grasping an object. Motor imagery is associated with the generation of electromagnetic brain activity. This brain activity for BCI control can be recorded by a set of electrode arrays placed on the scalp when employing electroencephalography (EEG, upper right), by electrode grids placed directly on the cortical surface when utilizing electrocorticography (ECoG, lower left), as well as by a set of sensors when using magnetoencephalography (MEG, lower right). (Images courtesy of the authors.)

Figure 4

SMR-BCI system (recoveriX) for motor recovery in stroke patients. This complete hardware and software platform that is capable of recording and analyzing the EEG for rehabilitation consists of: an electroencephalography system (EEG), an avatar (“virtual reality”) and a functional electrical stimulation (FES). The system provides the real-time, monitor-based virtual reality feedback (with an avatar and real-time brain activation maps). (Photographs courtesy of the authors.)