A Novel Digital Speller Based on a Hybrid Brain Computer Interface (hBCI) SSVEP with Eye Tracking

Many individuals with severe muscle atrophy caused by advanced Amyotrophic Lateral Sclerosis (ALS), brain stroke or spinal cord injury have a loss of their communication abilities. For those, who have problems or absence of speech capabilities, a speller system represents more than a tool as sometimes it is the only window that allows interaction with others and with the world. The objective of this work is to evaluate the performance of a speller in Brazilian Portuguese, based on a Novel hybrid Brain Computer Interface (hBCI), which uses Steady State Visual Evoked Potentials (SSVEP) from Electroencephalography (EEG) signals in addition to eye tracking acquired by a camera system with near field infrared light (NIR). The system uses a combination of both informations to ensure that a word is not misspelled. This way, it’s expected a reduction on the time to spell a word and on the user’s frustration, for having to correct mistyped letters. The interface also allows individuals with severe muscle palsy to communicate using only the eyes. This speller is particularly special because until the writing of this article there was no parallel in the Brazilian language. The tests were conducted with three volunteers who were asked to spell four words of everyday use on three trials. The experiment used en Emotiv EPOC + wireless device to obtain EEG signals and a Tobii Eyetracker 4C to obtain eye movements. The results obtained with the system are promising; the participants were able to perform the spelling tasks with an average typing speed of 4.2 characters per second and an accuracy of 80%.

[1]  Piotr Stawicki,et al.  A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI , 2017, Brain sciences.

[2]  Agostino Gibaldi,et al.  Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research , 2016, Behavior Research Methods.

[3]  Toshihisa Tanaka,et al.  Frequency recognition of steady-state visually evoked potentials using binary subband canonical correlation analysis with reduced dimension of reference signals , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  Tae-Seong Kim,et al.  An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier , 2015, Comput. Biol. Medicine.

[5]  A. Chiò,et al.  Projected increase in amyotrophic lateral sclerosis from 2015 to 2040 , 2016, Nature Communications.

[6]  Brendan Z. Allison,et al.  The Hybrid BCI , 2010, Frontiers in Neuroscience.

[7]  Teodiano Bastos,et al.  Comparison of the influence of stimuli color on Steady-State Visual Evoked Potentials , 2015 .

[8]  Sridhar Krishnan,et al.  An independent-BCI based on SSVEP using Figure-Ground Perception (FGP) , 2016, Biomed. Signal Process. Control..

[9]  K. Jellinger Toward Brain-Computer Interfacing , 2009 .

[10]  Toshihisa Tanaka,et al.  SSVEP frequency detection methods considering background EEG , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

[11]  Wei Wu,et al.  Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs , 2006, IEEE Transactions on Biomedical Engineering.

[12]  Yijun Wang,et al.  Learning to control an SSVEP-based BCI speller in naïve subjects , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  Soosan Beheshti,et al.  A new approach for SSVEP detection using PARAFAC and canonical correlation analysis , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  Abdellah Yousfi,et al.  The Context in Automatic Spell Correction , 2015 .

[15]  Sandra M. T. Muller,et al.  A comparison of techniques and technologies for SSVEP classification , 2014, 5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC).

[16]  Teodiano Freire Bastos Filho,et al.  Comparison between wire and wireless EEG acquisition systems based on SSVEP in an Independent-BCI , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Mihaly Benda,et al.  Brain–Computer Interface Spellers: A Review , 2018, Brain sciences.