Framework for Thought to Text Classification
DOI:
https://doi.org/10.61841/qjng9v31Keywords:
EEG Signals, Machine Learning, Imagined Words, KNN Classifier, Random Forest ClassifierAbstract
People with neurological disorders are unable to communicate their basic requirements because they lost the ability to speak. Designing a brain-computer interface that could convey their basic needs would make their lives easier. This article presents a system to determine the patient’s imagined words in the brain without him/her physically expressing them by EEG signal and machine learning. The imagined words in the mind-to-text mapping are converted into a classification problem among a predesigned set of words and classified by using machine learning algorithms. The decoding/classification of EEG signals to identify imagined words is carried out by KNN classifier and random forest classifier. The classification accuracy shows that the random forest classifier achieved better classification accuracy in comparison with KNN.
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References
[1] Conference: R. M. Mehmood and H. J. Lee, "Emotion classification of EEG brain signal using SVM and
KNN," 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Turin, 2015, pp.
1-5.
[2] Conference: D‟Zmura M, Deng S, LappasT, Thorpe S, Srinivasan R. (2009) Toward EEG Sensing of
Imagined Speech. In: Jacko, J.A. (eds.) Human-Computer Interaction. New Trends. HCI 2009. Lecture notes
in Computer Science. Vol 5610. Springer, Berlin, Heidelberg
[3] Journal Paper: A Sereshkeh, Trott, A Bricout and Chau, “EEG Classification of Covert
Speech Using Regularized Neural Networks,” in IEEE/ACM Transactions on Audio, Speech and Language
Processing.
[4] Journal Paper: Hashim, Noramiza& Ali, Aziah & Mohd-Isa, Wan-Noorshahida. (2018). Word-Based
Classification of Imagined Speech Using EEG. 10.1007/978-981-10-8276-4_19.
[5] Journal Paper: B. Min, J. Kim, H. J. Park, and B. Lee, “Vowel Imagery Decoding Toward Silent Speech
BCI Using Extreme Learning Machine with Electroencephalogram,” Biomed Res. Int., vol. 2016, pp. 1–11,
2016.
[6] Journal Paper: R. Kamalakkannan and R. Rajkumar, “Imagined Speech Classification using EEG,” Adv.
Biomed. Sci. Eng., vol. 1, no. 2, pp. 20-32, 2014.
[7] Journal Paper: O. Barak, K. Nishith, and M. Chopra, “Classifying Syllables in Imagined Speech using
EEG Data,” pp. 1–5, 2014.
[8] Conference: Cao, Mengsi et al. “EEG-based emotion recognition in Chinese emotional words.” 2011 IEEE
International Conference on Cloud Computing and Intelligence Systems (2011): 452-456.
[9] Journal Paper: Vijayakumar, Malarvizhi, “Fuzzy Logic Based Decision System for Context Aware
Cognitive Waveform Generation,” Wireless Personal Communications , 94(4), pp. 2681–2703, June 2017
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