Plenary Lecture

Brain Computer Interface (BCI) Using Tensor Decompositions Technology

Professor Andrzej Cichocki
Senior Team Leader and Head of
Laboratory for Advanced Brain Signal Processing
Riken, Brain Science Institute

Abstract: In this talk we will review several promising paradigms for Brain Computer Interface, (including P300/N170 ERPs, SSVEP, and motor imagery-MI paradigms) and novel multi-way (tensor) signal processing tools for EEG-BCI and analysis of brain to brain couplings/interactions (BBC/I). We will discuss how tensor (multiway arrays) can be applied for classification and recognition of evoked and event related potentials (EP/ERP). We illustrate this by Multiway Canonical Correlation Analysis (MCCA) which is applied to improve recognition rate of Steady State Visual Evoked Potentials (SSVEP). Furthermore, we will present affective brain-computer interfaces (aBCI) based on oddball paradigm using visual stimuli with emotional facial images and short video-clips. Our experiments confirmed that the face-sensitive event-related potential (ERP) components N170 and vertex positive potential (VPP) have reflected early structural encoding of emotional faces and allows us to improve performance and reliability of BCI. The developed multiway (tensor) signal processing tools are very promising not only for BCI but also for near-real time neurofeedback (NF) and EEG hyper-scanning to investigate human emotions, social interactions, brain to brain couplings/interactions and big data analysis in brain science.

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