Impact of Spatial Filters During Sensor Selection in a Visual P300 Brain-Computer Interface

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

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Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface

PLoS ONE ◽

10.1371/journal.pone.0067543 ◽

2013 ◽

Vol 8 (7) ◽

pp. e67543 ◽

Cited By ~ 10

Author(s):

David Feess ◽

Mario M. Krell ◽

Jan H. Metzen

Keyword(s):

Brain Computer Interface ◽

Sensor Selection ◽

Computer Interface ◽

Selection Mechanisms

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The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment

Computers ◽

10.3390/computers9030068 ◽

2020 ◽

Vol 9 (3) ◽

pp. 68

Author(s):

Patrick Schembri ◽

Maruisz Pelc ◽

Jixin Ma

Keyword(s):

Brain Computer Interface ◽

Low Cost ◽

User Preference ◽

Computer Interface ◽

Oddball Paradigm ◽

Active Listening ◽

P300 Speller ◽

Non Invasive ◽

Auditory Distractions ◽

Visual P300

This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications.

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