M. Kamrunnahar

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Noninvasive brain-computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar(More)
The aim of this study is to compare 2 EEG pattern classification methods towards the development of BCI. The methods are: (1) discriminant stepwise, and (2) Principal Component Analysis (PCA)-Linear Discriminant Analysis (LDA) joint method. Both methods use Fisher's LDA approach, but differ in the data dimensionality reduction procedure. Data were recorded(More)
Novel model based features are introduced in the discrimination of motor imagery tasks using human scalp electroencephalography (EEG) towards the development of Brain Computer Interfaces (BCI). We have acquired human scalp EEG under open-loop and feedback conditions in response to cue-based motor imagery tasks. EEG signals, transformed into frequency(More)
What is the optimal number of electrodes one can use in discrimination of tasks for a Brain Computer Interface (BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The(More)
For synchronous brain-computer interface (BCI) paradigms tasks that utilize visual cues to direct the user, the neural signals extracted by the computer are representative of voluntary modulation as well as evoked responses. For these paradigms, the evoked potential is often overlooked as a source of artifact. In this paper, we put forth the hypothesis that(More)
A new formulation of principal component analysis (PCA) that considers group structure in the data is proposed as a Variable Subset Selection (VSS) method. Optimization of electrode channels is a key problem in brain-computer interfaces (BCI). BCI experiments generate large feature spaces compared to the sample size due to time limitations in EEG sessions.(More)
For brain-computer interfaces (BCIs) that utilize visual cues to direct the user, the neural signals extracted by the computer are representative of ongoing processes, visual evoked responses, and voluntary modulation. We proposed to use three brain signatures for predicting success on a single trial of a BCI task. The first two features, the amplitude and(More)
A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the(More)
We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left(More)
We here studied the efficacy of wide-band frequency spectra (WBFS) features using multi-taper (MT) spectral analysis in application to motor imagery based Brain Computer Interfaces. We acquired motor imagery task related human scalp electroencephalography (EEG) signals for left vs. right hand movements using 3 different pairs of visual arrow cues. Left vs.(More)
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