Learning to control brain rhythms: making a brain-computer interface possible

Abstract

The ability to control electroencephalographic rhythms and to map those changes to the actuation of mechanical devices provides the basis for an assistive brain-computer interface (BCI). In this study, we investigate the ability of subjects to manipulate the sensorimotor mu rhythm (8-12-Hz oscillations recorded over the motor cortex) in the context of a rich visual representation of the feedback signal. Four subjects were trained for approximately 10 h over the course of five weeks to produce similar or differential mu activity over the two hemispheres in order to control left or right movement in a three-dimensional video game. Analysis of the data showed a steep learning curve for producing differential mu activity during the first six training sessions and leveling off during the final four sessions. In contrast, similar mu activity was easily obtained and maintained throughout all the training sessions. The results suggest that an intentional BCI based on a binary signal is possible. During a realistic, interactive, and motivationally engaging task, subjects learned to control levels of mu activity faster when it involves similar activity in both hemispheres. This suggests that while individual control of each hemisphere is possible, it requires more learning time.

Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly

“…Since alpha power suppression is positively correlated with cognitive performance [33] and beta band activity is associated with different cognitive and motor functions [3], training these spectral bands could modify neurocognitive functions.…”

“…These endogenous BCI systems depend on the user's ability to control the amplitude in a specific frequency band of the EEG recorded over a particular cortical area [45]. MI-BCI is based on the generation of event-related desynchronization (ERD) and event-related synchronization (ERS) in alpha (8)(9)(10)(11)(12)(13) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency bands of the EEG [20,33]. These events are related to sensorimotor rhythms (SMR).…”

“…Although it is an unusual method, learning control of brain rhythms using a BCI system has also been studied [33]. Similarly, there are also studies that focus on neuropsychological changes due to NFT in elderly people [1,36,41].…”

Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games

“…Examples of games and virtual environments using imaginary movement [30], P300 [2], and SSVEP [21] paradigms respectively. To elicit the P300 potential, blinking spheres were connected with each controllable object in the room.…”

“…The Berlin Brain-Computer Interface [14] has used motor imagery to play Pacman and Pong and similarly familiar games such as Tetris. Motor imagery applications exist for a First-Person Shooter game [30], navigating a ball in an environment where the ball has to jump over hills [24], navigating in Second Life [13] or other virtual environments, or controlling Google Earth [34]. Controlling the flippers of a virtual pinball machine by motor imagery also seems to be a promising application.…”