Abstract
BackgroundState-of-the-art signal processing methods are known to detect information in single-trial event-related EEG data, a crucial aspect in development of real-time applications such as brain computer interfaces. This paper investigates one such novel approach, evaluating how individual classifier and feature subset tailoring affects classification of single-trial EEG finger movements. The discrete wavelet transform was used to extract signal features that were classified using linear regression and non-linear neural network models, which were trained and architecturally optimized with evolutionary algorithms. The input feature subsets were also allowed to evolve, thus performing feature selection in a wrapper fashion. Filter approaches were implemented as well by limiting the degree of optimization.ResultsUsing only 10 features and 100 patterns, the non-linear wrapper approach achieved the highest validation classification accuracy (subject mean 75%), closely followed by the linear wrapper method (73.5%). The optimal features differed much between subjects, yet some physiologically plausible patterns were observed.ConclusionHigh degrees of classifier parameter, structure and feature subset tailoring on individual levels substantially increase single-trial EEG classification rates, an important consideration in areas where highly accurate detection rates are essential. Also, the presented method provides insight into the spatial characteristics of finger movement EEG patterns.
Highlights
State-of-the-art signal processing methods are known to detect information in single-trial event-related EEG data, a crucial aspect in development of real-time applications such as brain computer interfaces
The random feature selection achieved a significantly lower score than the high-performing wrapper classifiers, whereas not enough subjects were included to establish any significance of the observed difference between filter and random or wrapper
This study has demonstrated that individual classifier tailoring and feature subset selection significantly improves
Summary
State-of-the-art signal processing methods are known to detect information in single-trial event-related EEG data, a crucial aspect in development of real-time applications such as brain computer interfaces. The resulting collection of signals is highly complex, being multivariate, non-stationary, extremely noisy and highdimensional [1] These inherent properties result in analysis difficulties traditionally overcome by offline averaging of numerous events, time-fixed to a stimulus. Machine learning approaches provide tools for detection and classification of cortical patterns in real time. These methods are valuable for efficient data dimensionality reduction and feature selection, issues which receive growing attention in neuroscience as hardware technology, multi-channel EEG and fMRI, offers increasingly improved spatial and temporal resolution.
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