Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)

Muhammad Saad Bin Abdul Ghaffar; Umar Shahbaz Khan; Javaid Iqbal; Nasir Rashid; Amir Hamza; Waqar S. Qureshi; Mohsin Islam Tiwana; Umer Izhar

doi:10.1016/j.infrared.2020.103589

JOURNAL ARTICLE

Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)

Muhammad Saad Bin Abdul Ghaffar Umar Shahbaz Khan Javaid Iqbal Nasir Rashid Amir Hamza Waqar S. Qureshi Mohsin Islam Tiwana Umer Izhar

Year: 2020 Journal: Infrared Physics & Technology Vol: 112 Pages: 103589-103589 Publisher: Elsevier BV

DOI: 10.1016/j.infrared.2020.103589

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Keywords:

Computer science Kurtosis Support vector machine Mel-frequency cepstrum Pattern recognition (psychology) Artificial intelligence Brain–computer interface Linear discriminant analysis Preprocessor Skewness Frequency domain k-nearest neighbors algorithm Feature extraction Feature (linguistics) SIGNAL (programming language) Speech recognition Feature vector Time domain Computer vision Mathematics Statistics Electroencephalography

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EEG and Brain-Computer Interfaces

Life Sciences → Neuroscience → Cognitive Neuroscience

Non-Invasive Vital Sign Monitoring

Physical Sciences → Engineering → Biomedical Engineering

Optical Imaging and Spectroscopy Techniques

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)

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