Conversion of EEG activity into cursor movement by a brain-computer interface (BCI)

Abstract

The Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.

Distributed cortical adaptation during learning of a brain–computer interface task

“…brain-machine interface | motor learning | plasticity | electrophysiology | high gamma O ver the last 50 years, it has been demonstrated that, when given feedback, the brain can learn to volitionally modulate the activity of single neurons (1)(2)(3)(4)(5) and populations of neurons (6)(7)(8)(9). This modulation can occur in the absence of overt movement (6,9) or even when overt movement is not possible (3,10).…”

Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis

“…This parameter has also varied considerably. For example, Fabiani et al (2004) used 200 msec while Kelley et al (2002) used 1500 msec. Studies that use adaptive algorithms to estimate autoregressive coefficients (e.g., Schlogl et al, 2005) can be regarded as employing an exponential data window.…”

Single tap identification for fast BCI control

“…In general the AUCs are in the range of 0.6-0.9 which compares well with analogous 1-dimensional motor imagery BCI paradigms such as (Fabiani et al 2004) which achieves accuracies of between 71.8 and 74.3% or (Ang 2008) which achieves a mean accuracy of 73.3 ± 2.8%.…”