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Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI

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Document pages: 8 pages

Abstract: Recently, substantial progress has been made in the area of Brain-ComputerInterface (BCI) using modern machine learning techniques to decode andinterpret brain signals. While Electroencephalography (EEG) has provided anon-invasive method of interfacing with a human brain, the acquired data isoften heavily subject and session dependent. This makes seamless incorporationof such data into real-world applications intractable as the subject andsession data variance can lead to long and tedious calibration requirements andcross-subject generalisation issues. Focusing on a Steady State Visual EvokedPotential (SSVEP) classification systems, we propose a novel means ofgenerating highly-realistic synthetic EEG data invariant to any subject,session or other environmental conditions. Our approach, entitled the SubjectInvariant SSVEP Generative Adversarial Network (SIS-GAN), produces syntheticEEG data from multiple SSVEP classes using a single network. Additionally, bytaking advantage of a fixed-weight pre-trained subject classification network,we ensure that our generative model remains agnostic to subject-specificfeatures and thus produces subject-invariant data that can be applied to newpreviously unseen subjects. Our extensive experimental evaluation demonstratesthe efficacy of our synthetic data, leading to superior performance, withimprovements of up to 16 percentage points in zero-calibration classificationtasks when trained using our subject-invariant synthetic EEG signals.

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