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1

Lee, Chulhee. Feature extraction and classification algorithms for high dimensional data. School of Electrical Engineering, Purdue University, 1993.

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2

Eyben, Florian. Real-time Speech and Music Classification by Large Audio Feature Space Extraction. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27299-3.

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3

Ding, Yao, Zhili Zhang, Haojie Hu, Fang He, Shuli Cheng, and Yijun Zhang. Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8009-9.

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4

K, Sharma R., Munish Kumar, Manish Kumar Jindal, Simpel Rani Jindal, and Anupam Garg. Feature Extraction and Classification Techniques for Text Recognition. IGI Global, 2020.

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5

Kumar, Munish. Feature Extraction and Classification Techniques for Text Recognition. IGI Global, 2020.

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6

K, Sharma R., Munish Kumar, Manish Kumar Jindal, Simpel Rani Jindal, and Anupam Garg. Feature Extraction and Classification Techniques for Text Recognition. IGI Global, 2020.

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7

K, Sharma R., Munish Kumar, Manish Kumar Jindal, Simpel Rani Jindal, and Anupam Garg. Feature Extraction and Classification Techniques for Text Recognition. IGI Global, 2020.

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8

K, Sharma R., Munish Kumar, Manish Kumar Jindal, Simpel Rani Jindal, and Anupam Garg. Feature Extraction and Classification Techniques for Text Recognition. IGI Global, 2020.

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9

Wai Yie Leong, ed. EEG Signal Processing: Feature extraction, selection and classification methods. Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/pbhe016e.

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10

Leong, Wai Yie. EEG Signal Processing: Feature Extraction, Selection and Classification Methods. Institution of Engineering & Technology, 2019.

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11

Leong, Wai Yie. EEG Signal Processing: Feature extraction, selection and classification methods. The Institution of Engineering and Technology, 2019.

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12

Kumar, 1st Neelam Sanjeev, 2nd P. Nirmalkumar, and 3rd Kareemullah H. Feature Extraction and Classification in Context of Machine Learning. INSC International Publisher (IIP), 2021.

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13

Wang, Ran, and Jorge A. Chan-Lau. UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification. International Monetary Fund, 2020.

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14

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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15

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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16

Wang, Ran, and Jorge A. Chan-Lau. UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification. International Monetary Fund, 2020.

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17

Eyben, Florian. Real-Time Speech and Music Classification by Large Audio Feature Space Extraction. Springer London, Limited, 2016.

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18

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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19

Wang, Ran, and Jorge A. Chan-Lau. UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification. International Monetary Fund, 2020.

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20

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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21

Eyben, Florian. Real-time Speech and Music Classification by Large Audio Feature Space Extraction. Springer, 2018.

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22

Eyben, Florian. Real-time Speech and Music Classification by Large Audio Feature Space Extraction. Springer, 2017.

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23

Kumar, Nilesh, Nilesh Ramesh Kulkarni, and Vinayak Bairagi. EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques. Elsevier Science & Technology Books, 2018.

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24

Bairagi, Vinayak, and Nilesh Kulkarni. EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques. Elsevier Science & Technology Books, 2018.

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25

Jähne, Bernd, Rudolf Mester, Erhardt Barth, and Hanno Scharr. Complex Motion: First International Workshop, IWCM 2004, Günzburg, Germany, October 12-14, 2004, Revised Papers. Springer London, Limited, 2007.

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26

Prasad, Girijesh. Brain–machine interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0049.

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Abstract:
A brain–machine interface (BMI) is a biohybrid system intended as an alternative communication channel for people suffering from severe motor impairments. A BMI can involve either invasively implanted electrodes or non-invasive imaging systems. The focus in this chapter is on non-invasive approaches; EEG-based BMI is the most widely investigated. Event-related de-synchronization/ synchronization (ERD/ERS) of sensorimotor rhythms (SMRs), P300, and steady-state visual evoked potential (SSVEP) are the three main cortical activation patterns used for designing an EEG-based BMI. A BMI involves multiple stages: brain data acquisition, pre-processing, feature extraction, and feature classification, along with a device to communicate or control with or without neurofeedback. Despite extensive research worldwide, there are still several challenges to be overcome in making BMI practical for daily use. One such is to account for non-stationary brainwaves dynamics. Also, some people may initially find it difficult to establish a reliable BMI with sufficient accuracy. BMI research, however, is progressing in two broad areas: replacing neuromuscular pathways and neurorehabilitation.
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