03835nas a2200301 4500008004100000022001400041245015300055210006900208260001200277520274700289653005303036653004003089653003503129653003403164653003303198653003503231100002003266700002203286700002603308700002103334700001903355700002103374700001903395700002603414700002103440700002403461856004803485 2015 eng d a1872-895200aP300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.0 aP300based braincomputer interface BCI eventrelated potentials ER c02/20153 a

OBJECTIVE: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities.

METHODS: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6×6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects.

RESULTS: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN).

CONCLUSIONS: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate).

SIGNIFICANCE: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

10aalternative and augmentative communication (AAC)10aamyotrophic lateral sclerosis (ALS)10aBrain-computer interface (BCI)10abrain-machine interface (BMI)10aelectroencephalography (EEG)10aevent-related potentials (ERP)1 aMcCane, Lynn, M1 aHeckman, Susan, M1 aMcFarland, Dennis, J.1 aTownsend, George1 aMak, Joseph, N1 aSellers, Eric, W1 aZeitlin, Debra1 aTenteromano, Laura, M1 aWolpaw, Jonathan1 aVaughan, Theresa, M uhttp://www.ncbi.nlm.nih.gov/pubmed/2570394002668nas a2200241 4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520188700244653002802131653003502159653003402194653003102228653004102259653001902300100001202319700002602331700002102357856004802378 2013 eng d a1741-255200aAdaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.0 aAdaptive Laplacian filtering for sensorimotor rhythmbased brainc c02/2013 a0160020 v103 aOBJECTIVE: Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an 'adaptive Laplacian (ALAP) filter', can provide better performance for SMR-based BCIs. APPROACH: An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. MAIN RESULTS: Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. SIGNIFICANCE: Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.10aassistive communication10abrain computer interface (BCI)10abrain-machine interface (BMI)10aelectroencephalogram (EEG)10aleave-one-out (LOO) cross-validation10aspatial filter1 aLu, Jun1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan uhttp://www.ncbi.nlm.nih.gov/pubmed/2322087902258nas a2200193 4500008004100000022001400041245006700055210006500122260001200187300001100199490000600210520166100216653003501877653003401912653003201946100001901978700001901997856004802016 2011 eng d a1941-118900aBrain-computer interfaces using electrocorticographic signals.0 aBraincomputer interfaces using electrocorticographic signals c10/2011 a140-540 v43 a

Many studies over the past two decades have shown that people and animals can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems measure specific features of brain activity and translate them into control signals that drive an output. The sensor modalities that have most commonly been used in BCI studies have been electroencephalographic (EEG) recordings from the scalp and single-neuron recordings from within the cortex. Over the past decade, an increasing number of studies has explored the use of electrocorticographic (ECoG) activity recorded directly from the surface of the brain. ECoG has attracted substantial and increasing interest, because it has been shown to reflect specific details of actual and imagined actions, and because its technical characteristics should readily support robust and chronic implementations of BCI systems in humans. This review provides general perspectives on the ECoG platform; describes the different electrophysiological features that can be detected in ECoG; elaborates on the signal acquisition issues, protocols, and online performance of ECoG-based BCI studies to date; presents important limitations of current ECoG studies; discusses opportunities for further research; and finally presents a vision for eventual clinical implementation. In summary, the studies presented to date strongly encourage further research using the ECoG platform for basic neuroscientific research, as well as for translational neuroprosthetic applications.

10aBrain-computer interface (BCI)10abrain-machine interface (BMI)10aelectrocorticography (ECoG)1 aSchalk, Gerwin1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/22273796