Learning subject-specific spatial and temporal filters for single-trial EEG classification

There are a wide variety of electroencephalography (EEG) analysis methods. Most of them are based on averaging over multiple trials in order to increase signal-to-noise ratio. The method introduced in this article is a single trial method. Our approach is based on the assumption that the "response of interest" to each task is smooth, and is contained in several sensor channels. We propose a two-stage preprocessing method. In the first stage, we apply spatial filtering by taking weighted linear combinations of the sensor measurements. In the second stage, we perform time-domain filtering. In both steps, we derive filters that maximize a class dissimilarity measure subject to regularizing constraints on the total variation of the average estimated signal (or, alternatively, on the signal's strength in time intervals where it is known to be absent). No other spatial or spectral assumptions with regard to the anatomy or sources were made.

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