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Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective.

, , , , , and . BI, volume 11309 of Lecture Notes in Computer Science, page 163-172. Springer, (2018)

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Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective., , , , , and . BI, volume 11309 of Lecture Notes in Computer Science, page 163-172. Springer, (2018)Decoding Speech Evoked Jaw Motion from Non-invasive Neuromagnetic Oscillations., , and . IJCNN, page 1-8. IEEE, (2020)Neural Speech Decoding for Amyotrophic Lateral Sclerosis., , , , , and . INTERSPEECH, page 2782-2786. ISCA, (2020)Automatic Speech Activity Recognition from MEG Signals Using Seq2Seq Learning., , , and . NER, page 340-343. IEEE, (2019)Decoding Speech from Single Trial MEG Signals Using Convolutional Neural Networks and Transfer Learning., , , and . EMBC, page 5531-5535. IEEE, (2019)Magnetometers vs Gradiometers for Neural Speech Decoding., , , , , and . EMBC, page 6543-6546. IEEE, (2021)Visual Decoding of Phrases from Occipital Neuromagnetic Signals., , , , , and . NER, page 230-233. IEEE, (2021)Imagined, Intended, and Spoken Speech Envelope Synthesis from Neuromagnetic Signals., , , and . SPECOM, volume 12997 of Lecture Notes in Computer Science, page 134-145. Springer, (2021)Role of Brainwaves in Neural Speech Decoding., , and . EUSIPCO, page 1357-1361. IEEE, (2020)Towards decoding speech production from single-trial magnetoencephalography (MEG) signals., , , , and . ICASSP, page 3036-3040. IEEE, (2017)