Instantiating the onEEGwaveLAD Framework for Real-Time Muscle Artefact Identification and Mitigation in EEG Signals.

While electroencephalography is extremely useful for studying brain activity, EEG data is always contaminated by a wide range of artefacts. Many techniques exist to identify and remove such artefacts, primarily offline, with and without human supervision and intervention. This research presents a novel, fully automated online wavelet-based learning adaptive denoiser for artefact identification and mitigation in EEG signals. It contributes to knowledge by offering a framework that can be instantiated with artefact-specific and context-dependent parameters. In detail, this framework is instantiated for block online muscle artefact identification and mitigation. It is based on the discrete wavelet transformation (DWT) for time-frequency enrichment and the Isolation Forest algorithm for linearly learning data characteristics and identifying anomalous activity in a sliding moving buffer. It is built upon a denoising strategy that operates in the domain of DWT coefficients before reverting characteristics to the time domain. The findings demonstrate that such instantiation is promising in its goal of successfully identifying myogenic muscle movements and transforming them into cleaner EEG signals. They also emphasise the difficulties in tackling the known problem of the cone of influence associated with wavelet transformation and the tradeoff between the length of consecutive EEG windows and the problem's real-time nature.