Distributed signal processing algorithms for wireless EEG sensor networks with applications in auditory-based brain-computer interfaces.

Electroencephalography (EEG) is a cheap and non-invasive neuromonitoring technique to measure electrical potentials generated by the brain. Recently, the concept of a wireless EEG sensor network (WESN) has been proposed, in which the head is covered with a multitude of EEG nodes with facilities for local signal processing (SP) and wireless communication. Since they are amenable to extreme miniaturization and low-power system design, it is believed that such WESNs are an enabling technology for long-term wearable EEG monitoring. Since the wireless transmission is the most power-hungry component, it is crucial to minimize the amount of data that is to be transmitted. To this end, we will develop novel distributed algorithms to solve multi-channel EEG SP tasks, while avoiding energy-inefficient data centralization. We will focus on algorithms for artifact removal and extraction of brain responses, in particular for two different auditory-based brain-computer interfaces (BCIs).

Keywords:Distributed signal processing algorithms, brain-computer interfaces, wireless EEG sensor networks, auditory-based

Disciplines:Metallurgical engineering