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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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Spatial sparsification and low rank projection for fast analysis of multi-subject resting state fMRI data., , , and . ISBI, page 1280-1283. IEEE, (2018)Subject Generalization in Classifying Imagined and Spoken Speech with MEG., , , , , and . NER, page 1-4. IEEE, (2023)Automatic Recognition of Resting State fMRI Networks with Dictionary Learning., , , and . BI, volume 11309 of Lecture Notes in Computer Science, page 249-259. Springer, (2018)How Many FMRI Scans are Necessary and Sufficient for Resting Brain Connectivity Analysis?, , , and . GlobalSIP, page 494-498. IEEE, (2018)Spatial and Spectral Fingerprint in the Brain: Speaker Identification from Single Trial MEG Signals., , and . INTERSPEECH, page 1203-1207. ISCA, (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)Role of Brainwaves in Neural Speech Decoding., , and . EUSIPCO, page 1357-1361. IEEE, (2020)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)Towards a Speaker Independent Speech-BCI Using Speaker Adaptation., , , and . INTERSPEECH, page 864-868. ISCA, (2019)