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Dynamic functional brain network connectivity during pseudoword processing relates to children’s reading skill
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Computational Models in Electroencephalography.
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In: Brain topography, vol 35, iss 1 (2022)
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Functional Brain Networks and Verbal Fluency in Healthy Ageing ...
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Unified Coding of Spectral and Temporal Phonetic Cues: Electrophysiological Evidence for Abstract Phonological Features ...
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A Preliminary Report of Network Electroencephalographic Measures in Primary Progressive Apraxia of Speech and Aphasia
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In: Brain Sciences; Volume 12; Issue 3; Pages: 378 (2022)
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Spectral Entropy Monitoring Accelerates the Emergence from Sevoflurane Anesthesia in Thoracic Surgery: A Randomized Controlled Trial
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In: Journal of Clinical Medicine; Volume 11; Issue 6; Pages: 1631 (2022)
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Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface
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In: Sensors; Volume 22; Issue 2; Pages: 535 (2022)
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Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible
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In: NeuroSci; Volume 3; Issue 2; Pages: 226-244 (2022)
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Unified Coding of Spectral and Temporal Phonetic Cues: Electrophysiological Evidence for Abstract Phonological Features
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A dimension reduction technique applied to regression on high dimension, low sample size neurophysiological data sets
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In: ISSN: 1471-2202 ; EISSN: 1471-2202 ; BMC Neuroscience ; https://hal.univ-grenoble-alpes.fr/hal-03374818 ; BMC Neuroscience, BioMed Central, 2021, 22 (1), ⟨10.1186/s12868-020-00605-0⟩ (2021)
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Abstract:
International audience ; Abstract Background A common problem in neurophysiological signal processing is the extraction of meaningful information from high dimension, low sample size data (HDLSS). We present RoLDSIS (regression on low-dimension spanned input space), a regression technique based on dimensionality reduction that constrains the solution to the subspace spanned by the available observations. This avoids regularization parameters in the regression procedure, as needed in shrinkage regression methods. Results We applied RoLDSIS to the EEG data collected in a phonemic identification experiment. In the experiment, morphed syllables in the continuum /da/–/ta/ were presented as acoustic stimuli to the participants and the event-related potentials (ERP) were recorded and then represented as a set of features in the time-frequency domain via the discrete wavelet transform. Each set of stimuli was chosen from a preliminary identification task executed by the participant. Physical and psychophysical attributes were associated to each stimulus. RoLDSIS was then used to infer the neurophysiological axes, in the feature space, associated with each attribute. We show that these axes can be reliably estimated and that their separation is correlated with the individual strength of phonemic categorization. The results provided by RoLDSIS are interpretable in the time-frequency domain and may be used to infer the neurophysiological correlates of phonemic categorization. A comparison with commonly used regularized regression techniques was carried out by cross-validation. Conclusion The prediction errors obtained by RoLDSIS are comparable to those obtained with Ridge Regression and smaller than those obtained with LASSO and SPLS. However, RoLDSIS achieves this without the need for cross-validation, a procedure that requires the extraction of a large amount of observations from the data and, consequently, a decreased signal-to-noise ratio when averaging trials. We show that, even though RoLDSIS is a simple technique, it is suitable for the processing and interpretation of neurophysiological signals.
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Keyword:
[SCCO.PSYC]Cognitive science/Psychology; [SCCO]Cognitive science; Dimension reduction; Discrete wavelet transform; Electroencephalography; Event-related potentials; High dimension low sample size problem; Linear regression; Phonemic categorization
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URL: https://hal.univ-grenoble-alpes.fr/hal-03374818/file/s12868-020-00605-0.pdf
https://hal.univ-grenoble-alpes.fr/hal-03374818
https://hal.univ-grenoble-alpes.fr/hal-03374818/document
https://doi.org/10.1186/s12868-020-00605-0
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Somatosensory contribution to audio-visual speech processing
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In: ISSN: 0010-9452 ; Cortex ; https://hal.archives-ouvertes.fr/hal-03320604 ; Cortex, Elsevier, 2021, 143, pp.195-204. ⟨10.1016/j.cortex.2021.07.013⟩ ; https://doi.org/10.1016/j.cortex.2021.07.013 (2021)
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An ERP index of real-time error correction within a noisy-channel framework of human communication.
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Good Scientific Practice in MEEG Research: Progress and Perspectives
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In: https://hal.archives-ouvertes.fr/hal-03494100 ; 2021 (2021)
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Modality switch effects emerge early and increase throughout conceptual processing: Evidence from ERPs ...
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Human processing of transmitted speech varying in perceived quality ... : Menschliche Verarbeitung von technisch übertragener Sprache in unterschiedlich wahrgenommener Qualität ...
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Sustained neural rhythms reveal endogenous oscillations supporting speech perception. ...
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Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study
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In: Brain Sciences ; Volume 11 ; Issue 10 (2021)
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Testing Low-Frequency Neural Activity in Sentence Understanding ...
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ВОЗДЕЙСТВИЕ ТЕХНОЛОГИЙ ВИРТУАЛЬНОЙ РЕАЛЬНОСТИ НА ПСИХОЭМОЦИОНАЛЬНОЕ СОСТОЯНИЕ ПАЦИЕНТОВ С АФАЗИЯМИ ... : The impact of virtual reality on the emotional state of patients with aphasia ...
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Testing Low-Frequency Neural Activity in Sentence Understanding
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