MIDNN- a classification approach for the EEG based motor imagery tasks using deep neural network

Author:

Tiwari Smita,Goel Shivani,Bhardwaj Arpit

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference48 articles.

1. Bansal D, Mahajan R (2019) EEG-based brain-computer Interfaces: cognitive analysis and control applications. Academic Press, New York

2. Tan D, Nijholt A (2010) Brain-computer interfaces and human-computer interaction. In: Brain-computer interfaces. Springer, pp 3–19

3. Yuan H, He B (2014) Brain–computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng 61(5):1425–1435

4. Niedermeyer E, Lopes da Silva FH (2005) Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins

5. Panicker RC, Puthusserypady S, Sun Y (2011) An asynchronous p300 bci with ssvep-based control state detection. IEEE Trans Biomed Eng 58(6):1781–1788

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