Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms

Abstract

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.

Keywords: BCI; BMI; Brain-computer interface; EEG; brain-machine interface; motor imagery; neural interface; sensorimotor rhythm.

Figure 1.

General concept diagram of a motor-imagery based brain-computer interface (BCI). A user imagines some motor action, but performs no actual physical movement. The imagery produces a measureable signal that can be recorded with EEG, filtered, and decoded to determine the user’s intent. Once an estimate of user intent is obtained, a variety of physical devices can be controlled as an artificial substitute or replacement for the user’s natural motor movement.

Figure 2.

Schematic diagram of an example control paradigm. As described in [15] a user controls a wireless quadcopter to fly through target hoops in three-dimensional space by imagining movement of left or right hands to turn left or right respectively, both hands to move up, or resting to move down.

Figure 3.

Concept of EEG source imaging based BCI. Source signals can be estimated from scalp EEG measurements in conjunction with the head conduction model and used to control a computer cursor.

Figure 4.

Source imaging of right and left hand MI tasks in source space (a) and sensor space (b). (c) Time-frequency representation of the C3 and C4 electrode waveforms capture the ERD and ERS phenomena occurring during these two tasks. Localization of this event-related activity to the motor cortex indicates that neural processes responsible for the BCI control signal originate in the sensorimotor cortex [96].

Figure 5.

Average one-vs-all classification results from three subjects comparing the source (ROI) and sensor data for the different MI tasks (Ext – Extension, Flex – Flexion, Sup – Supination, Pro – Pronation).

Figure 6.

Overview of conventional approach of stimulation followed by performance with online approach of simultaneous stimulation and brain-computer interface learning. (bottom right) Subject performance change between beginning of first session and end of last session (Session 3) for sham (n=6) (dotted line) and anodal stimulation (n=7) (solid line) subjects. Error bars represent standard error.

Figure 7.

(a) A conceptual diagram of the study design and the potential role of mind body awareness training (MBAT) in the context of a sensorimotor-rhythm-based BCI. The EEG signal that is produced from motor imaginations is depicted in the background of the figure. The yellow target bars displayed on the left and right sides of the figure, in addition to the red ball in the middle, represent the standard left vs. right cursor task that is used for initial one-dimensional BCI training. (b) Experimental paradigms. Subjects belong to one of two cohorts – MBAT practitioners and controls. All subjects undergo the same task progression starting with a left vs. right cursor task, and later with an up vs. down cursor task. Opaque dots on the figure represent the percentage of subjects (drawn to scale) who have passed each stage of the protocol. Translucent dots represent the original pool of subjects. (The impact of mind-body awareness training on the early learning of a brain-computer interface, K. Cassady, A. You, A. Doud, and B. He, Technology, vol. 2, no. 3, Copyright @ 2014 World Scientific Publishing Co./Imperial College Press)

Figure 8.

The ratios of weighted average slope measures for MBAT subject left-right performance as compared to control subject performance. The red, dashed line at 1 indicates no difference between the two cohorts evaluated. A star indicates a statistically significant difference between the MBAT and control cohorts. (Based on work from [164])