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Novel Computational and Recording Techniques for Studying Neuronal Oscillations Acquired with EEG/MEG

Novel Computational and Recording Techniques for Studying Neuronal Oscillations Acquired with EEG/MEG

This video was recorded at BBCI Workshop: Advances in Neurotechnologies, Berlin 2012. In the first part of the talk I will present a new type of EEG electrodes. Current mainstream EEG electrode setups in BCI research permit efficient recordings, but are often bulky and uncomfortable for subjects. Recently we introduced a novel type of EEG electrode, which is designed for an optimal wearing comfort. This novel electrode is not felt by the subject and therefore recordings of EEG are performed in a most convenient way. Moreover, the electrode is close to invisible to an external observer. This is important especially for the situations when discomfort/unnecessary attention can be aroused either in the person wearing EEG electrodes or in persons who observe a subject with EEG electrodes. In the second part of the talk I will present a novel algorithm for the extraction of neuronal oscillations. Neuronal oscillations have been shown to underlie various cognitive, perceptual and motor functions in the brain and their amplitude reactivity is used commonly in BCI research. However, studying these oscillations is notoriously difficult with EEG/MEG recordings due to a massive overlap of activity from multiple sources and also due to the strong background noise. I will present a novel method for the reliable and fast extraction of neuronal oscillations from multi-channel EEG/MEG/LFP recordings. The method is based on a linear decomposition of recordings: it maximizes the signal power at a peak frequency while simultaneously minimizing it at the neighboring, surrounding frequency bins. Such procedure leads to the optimization of signal-to-noise ratio and allows extraction of components with a characteristic "peaky" spectral profile, which is typical for oscillatory processes. We refer to this method as spatio-spectral decomposition (SSD). Due to the high accuracy and speed, we suggest that SSD can be used as a reliable method for the extraction of neuronal oscillations from multi-channel electrophysiological recordings.

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