Abstract
Minimum energy combination (MEC) is a widely used method for frequency recognition in steady state visual evoked potential based BCI systems. Although it can reach acceptable performances, this method remains sensitive to noise. This paper introduces a new technique for the improvement of the MEC method allowing ameliorating its Anti-noise capability. The Empirical mode decomposition (EMD) and the moving average filter were used to separate noise from relevant signals. The results show that the proposed BCI system has a higher accuracy than systems based on Canonical Correlation Analysis (CCA) or Multivariate Synchronization Index (MSI). In fact, the system achieves an average accuracy of about 99% using real data measured from five subjects by means of the EPOC EMOTIVE headset with three visual stimuli. Also by using four commands, the system accuracy reaches 91.78% with an information-transfer rate of about 27.18 bits/min.
Highlights
Paralyzed people with neuromuscular disorders lose the majority or the totality of their movement and expression abilities
The sample entropy (SampEn) calculates the probability that epochs of window length m that are similar within a tolerance r remain similar at the point [23]
A new amelioration to improve the robustness against the noise of the Minimum Energy Combination method was proposed
Summary
Paralyzed people with neuromuscular disorders lose the majority or the totality of their movement and expression abilities. This is the case of people with locked-in syndrome, Amyotrophic lateral sclerosis and Spinal cord injury. A Brain-Computer Interface (BCI) is a tool for mobility, communication and control assistance which can provide them with the possibility to interact with their surroundings [1]. The electroencephalography (EEG) is usually used in BCI field for brain activity measurements. This is mainly due to its time resolution efficient for real-time applications, its low cost compared to other technics and the possibility to wear an EEG headset everywhere
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