A time-frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses

Pattern Recognition Letters

Volumn 32, Issue 8, 2011, Pages 1145-1153

  Cecotti, Hubert ^a,b  

^a UNIVERSITY OF BREMEN   (Germany)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Convolution; Electroencephalogram (EEG); Fourier transform; Neural network; Spatial filters; Steady state visual evoked potential (SSVEP)

Indexed keywords

CLASSICAL NEURAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; EEG SIGNALS; ELECTROENCEPHALOGRAM (EEG); FOURIER; FREQUENCY DOMAINS; HIDDEN LAYERS; NETWORK TOPOLOGY; OFFLINE; OFFLINE CLASSIFICATION; PRE-PROCESSING; RECOGNITION RATES; SIGNAL CLASSIFICATION; SIGNAL TRANSFORMATION; SPATIAL FILTERS; STEADY-STATE VISUAL EVOKED POTENTIALS; TIME DOMAIN; TIME FREQUENCY; TIME SEGMENTS;

CONVOLUTION; ELECTRIC NETWORK TOPOLOGY; ELECTROENCEPHALOGRAPHY; FAST FOURIER TRANSFORMS; FREQUENCY DOMAIN ANALYSIS; NETWORK ARCHITECTURE; TIME DOMAIN ANALYSIS;

NEURAL NETWORKS;

EID: 79953119438     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.02.022     Document Type: Article

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