Novel Methods for EEG Visualization and Visualization

Here we present several methods for representing electroencephalography (EEG) signals in a manner that is intuitive to non-scientists, in order to improve communication about the meaning of the underlying recorded signal. To support the use of various forms of EEG signal, we have developed a signal processing pipeline to filter noise and extract relevant, user-selected features. We provide two forms of presentation: a virtual reality (VR) system with a spatial audio and visualization, and a physical head model capable of displaying location and frequency-specific data. Examples are given for applications of the full system.

[1]  W. Ray,et al.  EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. , 1985, Science.

[2]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[3]  Tzyy-Ping Jung,et al.  Real-time neuroimaging and cognitive monitoring using wearable dry EEG , 2015, IEEE Transactions on Biomedical Engineering.

[4]  R. Barry,et al.  EEG differences between eyes-closed and eyes-open resting conditions , 2007, Clinical Neurophysiology.

[5]  Yang Yu,et al.  A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller , 2015, IEEE Transactions on Biomedical Engineering.

[6]  V. Dreyer,et al.  Visual acuity. , 1974, Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde.

[7]  E. Lopez-Poveda.  Development of Fundamental Aspects of Human Auditory Perception , 2014 .

[8]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[9]  V. Gallese,et al.  Time course and specificity of sensory-motor alpha modulation during the observation of hand motor acts and gestures: a high density EEG study , 2010, Experimental Brain Research.

[10]  Mahmoud Hassan,et al.  EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome , 2015, PloS one.

[11]  W. David Hairston,et al.  Traumatic Brain Injury Detection Using Electrophysiological Methods , 2015, Front. Hum. Neurosci..

[12]  Wei Wu,et al.  Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs , 2007, IEEE Transactions on Biomedical Engineering.

[13]  R. Anghinah,et al.  Traumatic brain injury: An EEG point of view , 2017, Dementia & neuropsychologia.

[14]  A. Cichocki,et al.  Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives , 2010, Progress in Neurobiology.

[15]  Cornelis J. Stam,et al.  Structure out of chaos: Functional brain network analysis with EEG, MEG, and functional MRI , 2013, European Neuropsychopharmacology.

[16]  J. Gruzelier.  EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants , 2014, Neuroscience & Biobehavioral Reviews.

[17]  Aurel Gontean,et al.  Brain-Computer Interface review , 2016, 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC).