Communication neural prostheses aim to restore the ability to efficient communication to people with paralysis and ALS. These systems record neural signals from the brain and translate them, through a decoder algorithm, into control signals for moving an end effector. In our study, monkeys controlled computer cursors to acquire targets on a keyboard-like grid.
We extend previous EEG source imaging (ESI) work to decode four natural MI tasks of the right hand that may be utilized for realistic and intuitive BCI control. We report an increase of up to 18.6% for individual task classification, and an increase of 12.7% for the overall classification using the proposed ESI approach over the traditional sensor-based method.
Using a heteroscedastic matrix-variate Gaussian model, we introduce a novel theoretical approach for spatio-spectral generalization of the common spatial patterns (CSP) method. Compared to the alternative CSP generalizations, such as filterbank-CSP, the proposed algorithm has less computational cost and competent performance.
In order to overcome the high impedance barrier from ultrasmall recording sites, low impedance electrode coatings have been of significant interest. We report that a coating made of a new blend of PEDOT doped with carboxyl functionalized multi-walled carbon nanotubes demonstrated significant (p<0.05) long-term recording improvement over the traditional PEDOT/PSS formula on silicon lattice electrode arrays.
We introduced a novel EEG electrode that is composed of uniformly distributed carbon nanotube (CNT) embedded within adhesive polydimethylsiloxane (aPDMS). EEG could be measured accurately even with hairs on the contacting surface. With the capacitive coupling in the interface between metal and CNT/ aPDMS composite, the insulation layer is controllable. Lastly, to amend the EEG quality, pillars were implemented and Parylene C layer was deposited as an insulation layer.
Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output…