02118nas a2200217 4500008004100000245008100041210006900122520141400191653002301605653003501628653001901663653003201682100001701714700001901731700002001750700001501770700001801785700002001803700001901823856005801842 2015 eng d00aIdentifying the Attended Speaker Using Electrocorticographic (ECoG) Signals.0 aIdentifying the Attended Speaker Using Electrocorticographic ECo3 aPeople affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control. Thus, they cannot use traditional assistive communication devices that depend on muscle control, or brain-computer interfaces (BCIs) that depend on the ability to control gaze. While auditory and tactile BCIs can provide communication to such individuals, their use typically entails an artificial mapping between the stimulus and the communication intent. This makes these BCIs difficult to learn and use. In this study, we investigated the use of selective auditory attention to natural speech as an avenue for BCI communication. In this approach, the user communicates by directing his/her attention to one of two simultaneously presented speakers. We used electrocorticographic (ECoG) signals in the gamma band (70–170 Hz) to infer the identity of attended speaker, thereby removing the need to learn such an artificial mapping. Our results from twelve human subjects show that a single cortical location over superior temporal gyrus or pre-motor cortex is typically sufficient to identify the attended speaker within 10 s and with 77% accuracy (50% accuracy due to chance). These results lay the groundwork for future studies that may determine the real-time performance of BCIs based on selective auditory attention to speech.10aauditory attention10aBrain-computer interface (BCI)10aCocktail Party10aelectrocorticography (ECoG)1 aDijkstra, K.1 aBrunner, Peter1 aGunduz, Aysegul1 aCoon, W.G.1 aRitaccio, A L1 aFarquhar, Jason1 aSchalk, Gerwin uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776341/02257nas a2200217 4500008004100000022001400041245016900055210006900224260001200293520155100305653000801856653001301864653000801877653000801885653000801893653002201901653002301923100002001946700002501966856004801991 2013 eng d a1559-008900aInteractions Between Pre-Processing and Classification Methods for Event-Related-Potential Classification : Best-Practice Guidelines for Brain-Computer Interfacing.0 aInteractions Between PreProcessing and Classification Methods fo c04/20133 aDetecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g. visual or tactile), ERP component (e.g. P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.10aBCI10adecoding10aEEG10aERP10aLDA10aspatial filtering10aspectral filtering1 aFarquhar, Jason1 aHill, Jeremy, Jeremy uhttp://www.ncbi.nlm.nih.gov/pubmed/2325066802954nas a2200373 4500008004100000022001400041245008200055210006900137260001200206300001100218490000700229520184300236653002802079653002702107653002802134653002702162653002202189653003002211653001102241653001302252653002502265653002402290653001202314653004102326653002802367653001802395653002202413100001902435700001502454700002502469700002002494700001802514856004802532 2011 eng d a1530-888X00aA graphical model framework for decoding in the visual ERP-based BCI speller.0 agraphical model framework for decoding in the visual ERPbased BC c01/2011 a160-820 v233 a

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

10aArtificial Intelligence10aComputer User Training10aDiscrimination Learning10aElectroencephalography10aEvoked Potentials10aEvoked Potentials, Visual10aHumans10aLanguage10aModels, Neurological10aModels, Theoretical10aReading10aSignal Processing, Computer-Assisted10aUser-Computer Interface10aVisual Cortex10aVisual Perception1 aMartens, S M M1 aMooij, J M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B uhttp://www.ncbi.nlm.nih.gov/pubmed/2096454001758nas a2200361 4500008004100000022001400041245012300055210006900178260001200247300001100259490000600270520064200276653001500918653001000933653001400943653002400957653002700981653003501008653001101043653002501054653003501079653002301114653001401137653004101151653003401192653002801226653001201254100001901266700002501285700002001310700001801330856004801348 2009 eng d a1741-255200aOverlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.0 aOverlap and refractory effects in a braincomputer interface spel c04/2009 a0260030 v63 a

We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.

10aAlgorithms10aBrain10aCognition10aComputer Simulation10aElectroencephalography10aEvent-Related Potentials, P30010aHumans10aModels, Neurological10aPattern Recognition, Automated10aPhotic Stimulation10aSemantics10aSignal Processing, Computer-Assisted10aTask Performance and Analysis10aUser-Computer Interface10aWriting1 aMartens, S M M1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aSchölkopf, B uhttp://www.ncbi.nlm.nih.gov/pubmed/1925546202147nas a2200145 4500008004100000245008600041210006900127260001200196520161100208653003001819100002001849700002501869700001801894856008901912 2006 eng d00aOptimizing Spatial Filters for BCI: Margin- and Evidence-Maximization Approaches.0 aOptimizing Spatial Filters for BCI Margin and EvidenceMaximizati c11/20063 a

We present easy-to-use alternatives to the often-used two-stage Common Spatial Pattern + classifier approach for spatial filtering and classification of Event-Related Desynchronization signals in BCI. We report two algorithms that aim to optimize the spatial filters according to a criterion more directly related to the ability of the algorithms to generalize to unseen data. Both are based upon the idea of treating the spatial filter coefficients as hyperparameters of a kernel or covariance function. We then optimize these hyper-parameters directly along side the normal classifier parameters with respect to our chosen learning objective function. The two objectives considered are margin maximization as used in Support-Vector Machines and the evidence maximization framework used in Gaussian Processes. Our experiments assessed generalization error as a function of the number of training points used, on 9 BCI competition data sets and 5 offline motor imagery data sets measured in Tubingen. Both our approaches sho w consistent improvements relative to the commonly used CSP+linear classifier combination. Strikingly, the improvement is most significant in the higher noise cases, when either few trails are used for training, or with the most poorly performing subjects. This a reversal of the usual "rich get richer" effect in the development of CSP extensions, which tend to perform best when the signal is strong enough to accurately find their additional parameters. This makes our approach particularly suitable for clinical application where high levels of noise are to be expected.

10aBrain Computer Interfaces1 aFarquhar, Jason1 aHill, Jeremy, Jeremy1 aSchölkopf, B uhttp://www.researchgate.net/publication/237615110_Optimizing_Spatial_Filters_for_BCI01333nas a2200145 4500008004100000245004900041210004800090260001200138520093100150100002001081700002501101700001301126700001801139856003001157 2006 eng d00aRegularised CSP for Sensor Selection in BCI.0 aRegularised CSP for Sensor Selection in BCI c01/20063 a

The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to learn the noise in the training set rather than the signal. Both problems motivate an approach in which spatial filters are sparsified. We briefly sketch a reformulation of the problem which allows us to do this, using 1-norm regularisation. Focusing on the electrode selection issue, we present preliminary results on EEG data sets that suggest that effective spatial filters may be computed with as few as 10–20 electrodes, hence offering the potential to simplify the practical realisation of BCI systems significantly.

1 aFarquhar, Jason1 aHill, Jeremy, Jeremy1 aLal, T N1 aSchölkopf, B uhttp://edoc.mpg.de/31206001035nas a2200145 4500008004100000245005800041210005500099260001200154520061700166100002500783700002000808700001300828700001800841856003000859 2006 eng d00aTime-Dependent Demixing of Task-Relevant EEG Signals.0 aTimeDependent Demixing of TaskRelevant EEG Signals c09/20063 a

Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP) approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.

1 aHill, Jeremy, Jeremy1 aFarquhar, Jason1 aLal, T N1 aSchölkopf, B uhttp://edoc.mpg.de/312053