02171nas a2200289 4500008004100000022001400041245012100055210006900176260001200245300001100257490000700268520128300275653001501558653003001573653001401603653002101617653002701638653001601665653001101681653002201692653003201714100002001746700002201766700002101788700002401809856004801833 2015 eng d a1532-821X00aToward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users.0 aToward independent home use of braincomputer interfaces a decisi c03/2015 aS27-320 v963 a

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.

10aAlgorithms10abrain-computer interfaces10aCognition10aDisabled Persons10aElectroencephalography10aEnvironment10aHumans10aPatient Selection10aPhysical Therapy Modalities1 aKübler, Andrea1 aHolz, Elisa, Mira1 aSellers, Eric, W1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/2572154405892nas a2200361 4500008004100000022001400041245010100055210006900156260001200225300001000237490000800247520480000255653001505055653002805070653001805098653002005116653002705136653002405163653001105187653000905198653001105207653003105218653003205249653002805281653004105309653002205350653002805372100002305400700002005423700002005443700001905463856004805482 2008 eng d a0165-027000aExtraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.0 aExtraction and localization of mesoscopic motor control signals  c01/2008 a63-810 v1673 a

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.

10aAdolescent10aBiofeedback, Psychology10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEpilepsies, Partial10aFemale10aHand10aHumans10aMagnetic Resonance Imaging10aPhysical Therapy Modalities10aPsychomotor Performance10aSignal Processing, Computer-Assisted10aSpectrum Analysis10aUser-Computer Interface1 aSanchez, Justin, C1 aGunduz, Aysegul1 aCarney, Paul, R1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/17582507