Miniaturization Effects and Node Placement for Neural Decoding in EEG Sensor Networks
Electroencephalography or EEG can be used in a wide range of applications like diagnosing disorders like epilepsy, Brain Computer Interfaces or in neural prostheses. These mentioned applications would benefit from a continuous 24/7 EEG monitoring, but this is impractical with traditional bulky EEG headsets. On the other hand, the recent emergence of novel miniature and wearable EEG devices could make it possible, yet these devices come with the drawback that they only cover a small scalp area with a few EEG channels. However, deploying multiple such devices and wirelessly connecting them would allow to cover a larger scalp area without giving in on flexibility and miniaturization. We will call such a network of wirelessly connected EEG devices a wireless EEG sensor-network (WESNs).
Nevertheless, the signals collected by such a WESN will be very different from traditional EEG due to the far-driven miniaturization and the fact that all sensor nodes are galvanically separated. This project investigates whether optimizing a WESN topology can compensate for such miniaturization effects in a neural decoding task, namely, auditory attention decoding (AAD).