@article {3415, title = {Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users.}, journal = {Arch Phys Med Rehabil}, volume = {96}, year = {2015}, month = {03/2015}, pages = {S27-32}, abstract = {

Noninvasive brain-computer interfaces (BCIs) use scalp-recorded electrical activity from the brain to control an application. Over the past 20 years, research demonstrating that BCIs can provide communication and control to individuals with severe motor impairment has increased almost exponentially. Although considerable effort has been dedicated to offline analysis for improving signal detection and translation, far less effort has been made to conduct online studies with target populations. Thus, there remains a great need for both long-term and translational BCI studies that include individuals with disabilities in their own homes. Completing these studies is the only sure means to answer questions about BCI utility and reliability. Here we suggest an algorithm for candidate selection for electroencephalographic (EEG)-based BCI home studies. This algorithm takes into account BCI end-users and their environment and should assist in study design and substantially improve subject retention rates, thereby improving the overall efficacy of BCI home studies. It is the result of a workshop at the Fifth International BCI Meeting that allowed us to leverage the expertise of multiple research laboratories and people from multiple backgrounds in BCI research.

}, keywords = {Algorithms, brain-computer interfaces, Cognition, Disabled Persons, Electroencephalography, Environment, Humans, Patient Selection, Physical Therapy Modalities}, issn = {1532-821X}, doi = {10.1016/j.apmr.2014.03.036}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25721544}, author = {K{\"u}bler, Andrea and Holz, Elisa Mira and Sellers, Eric W and Theresa M Vaughan} } @article {2098, title = {Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.}, journal = {J Neurosci Methods}, volume = {167}, year = {2008}, month = {01/2008}, pages = {63-81}, abstract = {

Electrocorticogram (ECoG) recordings for\ neuroprosthetics\ provide a\ mesoscopic\ level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial\ ECoG\ neural interfaces require appropriate feature\ extraction\ and signal processing methods to identify and model in real-time signatures of\ motor\ events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz)\ ECoG\ from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased\ ECoG\ voltages to determine if they provide viable\ control\ inputs for\ ECoG\ neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the\ ECoG\ above the level of noisy background fluctuations in order to derive the commands for complex, continuous\ control\ tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for\ motor\ tasks using amplitude modulated\ ECoG.

}, keywords = {Adolescent, Biofeedback, Psychology, Brain Mapping, Cerebral Cortex, Electroencephalography, Epilepsies, Partial, Female, Hand, Humans, Magnetic Resonance Imaging, Physical Therapy Modalities, Psychomotor Performance, Signal Processing, Computer-Assisted, Spectrum Analysis, User-Computer Interface}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2007.04.019}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17582507}, author = {Sanchez, Justin C and Gunduz, Aysegul and Carney, Paul R and Principe, Jose} }