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

Title
Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms
Authors
Abibullaev, B[Abibullaev, Berdakh]An, J[An, Jinung]
DGIST Authors
Abibullaev, B[Abibullaev, Berdakh]; An, J[An, Jinung]
Issue Date
2012-12
Citation
Medical Engineering and Physics, 34(10), 1394-1410
Type
Article
Article Type
Article
Keywords
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
ISSN
1350-4533
Abstract
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.
URI
http://hdl.handle.net/20.500.11750/1615
DOI
10.1016/j.medengphy.2012.01.002
Publisher
Elsevier Ltd
Related Researcher
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Collection:
ETCETC
Division of IoT∙Robotics Convergence Research1. Journal Articles
ETC1. Journal Articles


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