Abdul Rehman Satti

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The non-stationary nature of the electroencephalogram (EEG) poses a major challenge for the successful operation of a brain-computer interface (BCI) when deployed over multiple sessions. The changes between the early training measurements and the proceeding multiple sessions can originate as a result of alterations in the subject’s brain process, new(More)
This paper shows for the first time how a popular and successful filtering approach, known as the common spatial patterns (CSP) approach, compares to the neural time series prediction preprocessing (NTSPP) approach when applied in a 2-class EEG-based brain-computer interface (BCI), either using 2 or 60 EEG channels. Additionally, a novel NTSPP-CSP(More)
This paper presents an overview of a multistage signal processing framework to tackle the main challenges in continuous control protocols for motor imagery based synchronous and self-paced BCIs. The BCI can be setup rapidly and automatically even when conducting an extensive search for subject-specific parameters. A new BCI-based game training paradigm(More)
Distinct features play a vital role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, numerous parameters, such as separable frequency bands, data acquisition channels and time point of maximum(More)
The consistency and reliability of the brain computer interface (BCI) system is often questioned to be safe for controlling a wheelchair as BCIs characteristically experience a low signal-to-noise ratio and low classification accuracy. Electroencephalogram (EEG) acquired non-invasively consists of multiple time-series which are highly correlated because of(More)
Recent work has shown that combining prediction based preprocessing based on neural-time-series-prediction-preprocessing (NTSPP) along with spectral filtering (SF) and common-spatial patterns (CSP) can significantly improve the performance of a motor imagery based brain-computer interface (BCI) involving two classes. This paper illustrates how these(More)
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