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Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms

Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms
Abibullaev, B[Abibullaev, Berdakh]An, J[An, Jinung]
DGIST Authors
Abibullaev, B[Abibullaev, Berdakh]An, J[An, Jinung]
Issued Date
Article Type
AdultAlgorithmAlgorithmsANNArtificial IntelligenceBrainBrain-Computer Interface (BCI)Brain Computer InterfaceBrain CortexClassificationClassifierCognitionContinuous Wavelet TransformDecompositionDiscriminant AnalysisFemaleFrontal CortexFrontal LobeFunctional Near-Infrared Spectroscopy (FNIRS)Functional NeuroimagingHemodynamicsHumansInfrared RadiationInterfaces (Computer)LDALearning AlgorithmsLearning SystemsMachine LearningMaleMathematical AnalysisMental TaskMental Task ClassificationNear-Infrared SpectroscopyNervous System FunctionNeural Networks (Computer)NeuroimagingNeuronsPriority JournalQuantitative AnalysisSignal ProcessingSpectroscopy, Near-InfraredSupport Vector MachinesSVMTask PerformanceWavelet AnalysisWavelet DecompositionWavelet Transforms
Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain-computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier. © 2012 IPEM.
Elsevier Ltd
Related Researcher
  • 안진웅 An, Jinung 지능형로봇연구부
  • Research Interests
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Appears in Collections:
ETC 1. Journal Articles
Division of Intelligent Robotics Brain Robot Augmented InteractioN(BRAIN) Laboratory 1. Journal Articles


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