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Phase-based SSVEPs for a Real-Time BCI
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Phase-based SSVEPs for a Real-Time Brain-Computer Interface (BCI)

Lead Investigator: Mr Norbert Gauci
Supervisor: Dr Owen Falzon, Centre for Biomedical Cybernetics
Co-Supervisor: Dr Tracey Camilleri, Dept. of Systems and Control Engineering, Faculty of Engineering


In recent years, brain-computer interface (BCI) systems have emerged as a technology that can be used to develop a direct communication pathway between the human brain and the external environment. Among the various neurophysiological phenomena that can be used to drive BCI systems, steady-state visual evoked potentials (SSVEPs) have gained increasing popularity because of the high information transfer rate and high accuracy that these can provide. SSVEPs are oscillatory responses in the electroencephalography (EEG) that are detected over the occipital and parietal regions of the brain. A typical SSVEP-based BCI system consists of a set of uniquely coded visual stimuli that when a user attends to them, evoke a distinctive SSVEP response, allowing them to make a specific selection.

Conventional SSVEP systems adopt frequency analysis techniques to extract magnitude characteristics that can be used to discriminate between multiple visual targets. Since SSVEPs are phase-locked to the target stimulus, phase analysis techniques have also been used in several SSVEP systems. In this work, an investigation was conducted on how the inclusion of phase information in the form of a novel proposed feature, the phase-weighted SNR (PWS) feature, could improve the performance of such BCIs. Improvements in classification accuracies of up to 17% were obtained with the inclusion of phase information when compared to systems that rely solely on amplitude information.

BCI motorised bed 

The results obtained from this study were used to design a real-time BCI application to control a motorised bed. The proposed phase-based SSVEP system consists of eight targets, seven visual stimuli and an idle state, that are presented to the users to evoke SSVEP response. Each visual stimulus is associated with a particular function of the motorised bed while the idle state represents the no action state where the bed remains stationary. The results achieved across eight participants are encouraging and demonstrate its potential to be used in a real-world environment. 


N. Gauci, O. Falzon, T. Camilleri and K. P. Camilleri, 'Phase-based SSVEPs for Real-Time Control of a Motorised Bed', in Proc. of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), Jeju, South Korea, July 2017

N. Gauci, O. Falzon, T. Camilleri, and K. Camilleri, "An Analysis on the Effect of Phase on the Performance of SSVEP-based BCIs", in IFMBE Proceedings, XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, vol. 57, pp. 134-139, Springer International Publishing, 2016. 

Other Links 

BCI research at the Dept. of Systems and Control Engineering [Link

Malta Brain Awareness Week 2018
Held between 12 and 18 of March 2018
Research at UoM: Brain Computer Interface

Communicating using brain signals

Last Updated: 31 August 2017

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