title: Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.
abstract: BACKGROUND
  Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful.
  
  
  METHODS
  In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.
  
  
  RESULTS
  Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.
  
  
  CONCLUSIONS
  The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.
authors.affiliation: Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia.
authors.forename: Rana Fayyaz
authors.initials: RF
authors.lastname: Ahmad
authors.affiliation: Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia.
authors.forename: Aamir Saeed
authors.initials: AS
authors.lastname: Malik
authors.affiliation: Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia.
authors.forename: Nidal
authors.initials: N
authors.lastname: Kamel
authors.affiliation: Department of Neuroscience, Universiti Sains Malaysia, Kota Bharu, Kelantan, Malaysia.
authors.forename: Faruque
authors.initials: F
authors.lastname: Reza
authors.affiliation: Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia.
authors.forename: Hafeez Ullah
authors.initials: HU
authors.lastname: Amin
authors.affiliation: Department of Computer Science, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Hussain
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging* / Q000502:physiology; D058256:Brain Waves / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010775:Photic Stimulation
keywords: EEG; classification; data fusion; fMRI; simultaneous EEG-fMRI