An Electrocorticographic Brain Interface in an Individual with Tetraplegia

Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals

Games and Brain-Computer Interfaces: The State of the Art

BCI gaming is a very young field; most games are proof-of-concepts. Work that compares BCIs in a game environments with traditional BCIs indicates no negative effects, or even a positive effect of the rich visual environments on the performance. The low transfer-rate of current games poses a problem for control of a game. This is often solved by changing the goal of the game. Multi-modal input with BCI forms an promising solution, as does assigning more meaningful functionality to BCI control

Noninvasive neural decoding of overt and covert hand movement

It is generally assumed that the signal-to-noise ratio and information content of neural data acquired noninvasively via magnetoencephalography (MEG) or scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multi-joint movements of the upper limb. If valid, this assumption could severely limit the practical usage of noninvasive signals in brain-computer interface (BCI) systems aimed at continuous complex control of arm-like prostheses for movement impaired persons. Fortunately this dissertation research casts doubt on the veracity of this assumption by extracting continuous hand kinematics from MEG signals collected during a 2D center-out drawing task (Bradberry et al. 2009, NeuroImage, 47:1691-700) and from EEG signals collected during a 3D center-out reaching task (Bradberry et al. 2010, Journal of Neuroscience, 30:3432-7). In both studies, multiple regression was performed to find a matrix that mapped past and current neural data from multiple sensors to current hand kinematic data (velocity). A novel method was subsequently devised that incorporated the weights of the mapping matrix and the standardized low resolution electromagnetic tomography (sLORETA) software to reveal that the brain sources that encoded hand kinematics in the MEG and EEG studies were corroborated by more traditional studies that required averaging across trials and/or subjects. Encouraged by the favorable results of these off-line decoding studies, a BCI system was developed for on-line decoding of covert movement intentions that provided users with real-time visual feedback of the decoder output. Users were asked to use only their thoughts to move a cursor to acquire one of four targets on a computer screen. With only one training session, subjects were able to accomplish this task. The promising results of this dissertation research significantly advance the state-of-the-art in noninvasive BCI systems

Control of the electric wheelchair using EEG classification

Electric wheelchairs are some of the most important devices to assist physically handicapped persons. This paper presents the concept of brain controlled electric wheelchair designed for people who are not able to use other interfaces such as a hand joystick, and in particular for patients suffering from amyotrophic lateral sclerosis (ALS). The objective is to control the direction of an electric wheelchair using noninvasive scalp electroencephalogram (EEG)