| Keyword search (4,163 papers available) | ![]() |
"electroencephalography" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Sound degradation type differentially affects neural indicators of cognitive workload and speech tracking | Gagné N; Greenlaw KM; Coffey EBJ; | 40412301 PSYCHOLOGY |
| 2 | Phase-Amplitude Coupling of NREM Sleep Oscillations Shows Between-Night Stability and is Related to Overnight Memory Gains | Cross N; O' Byrne J; Weiner OM; Giraud J; Perrault AA; Dang-Vu TT; | 40214027 PERFORM |
| 3 | PreVISE: an efficient virtual reality system for SEEG surgical planning | Spiegler P; Abdelsalam H; Hellum O; Hadjinicolaou A; Weil AG; Xiao Y; | 39735694 ENCS |
| 4 | Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology | Hrtonova V; Nejedly P; Travnicek V; Cimbalnik J; Matouskova B; Pail M; Peter-Derex L; Grova C; Gotman J; Halamek J; Jurak P; Brazdil M; Klimes P; Frauscher B; | 39608298 SOH |
| 5 | A protocol for trustworthy EEG decoding with neural networks | Borra D; Magosso E; Ravanelli M; | 39549492 ENCS |
| 6 | SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals | Borra D; Paissan F; Ravanelli M; | 39265481 ENCS |
| 7 | The neurophysiology of closed-loop auditory stimulation in sleep: A magnetoencephalography study | Jourde HR; Merlo R; Brooks M; Rowe M; Coffey EBJ; | 37675803 CONCORDIA |
| 8 | Dynamic networks differentiate the language ability of children with cochlear implants | Koirala N; Deroche MLD; Wolfe J; Neumann S; Bien AG; Doan D; Goldbeck M; Muthuraman M; Gracco VL; | 37409105 PSYCHOLOGY |
| 9 | Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data | Thölke P; Mantilla-Ramos YJ; Abdelhedi H; Maschke C; Dehgan A; Harel Y; Kemtur A; Mekki Berrada L; Sahraoui M; Young T; Bellemare Pépin A; El Khantour C; Landry M; Pascarella A; Hadid V; Combrisson E; O' Byrne J; Jerbi K; | 37385392 IMAGING |
| 10 | Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy | Frauscher B; Bénar CG; Engel JJ; Grova C; Jacobs J; Kahane P; Wiebe S; Zjilmans M; Dubeau F; | 37119580 PERFORM |
| 11 | Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain | Ocay DD; Teel EF; Luo OD; Savignac C; Mahdid Y; Blain-Moraes S; Ferland CE; | 36601627 HKAP |
| 12 | Alpha and beta neural oscillations differentially reflect age-related differences in bilateral coordination | Shih PC; Steele CJ; Nikulin VV; Gundlach C; Kruse J; Villringer A; Sehm B; | 33979705 PSYCHOLOGY |
| 13 | Fast oscillations >40 Hz localize the epileptogenic zone: An electrical source imaging study using high-density electroencephalography. | Avigdor T, Abdallah C, von Ellenrieder N, Hedrich T, Rubino A, Lo Russo G, Bernhardt B, Nobili L, Grova C, Frauscher B | 33450578 PERFORM |
| 14 | PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research | Parent M; Albuquerque I; Tiwari A; Cassani R; Gagnon JF; Lafond D; Tremblay S; Falk TH; | 33363449 PERFORM |
| 15 | Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter | Hamid L; Habboush N; Stern P; Japaridze N; Aydin Ü; Wolters CH; Claussen JC; Heute U; Stephani U; Galka A; Siniatchkin M; | 33250282 PERFORM |
| 16 | Localization Accuracy of Distributed Inverse Solutions for Electric and Magnetic Source Imaging of Interictal Epileptic Discharges in Patients with Focal Epilepsy. | Heers M, Chowdhury RA, Hedrich T, Dubeau F, Hall JA, Lina JM, Grova C, Kobayashi E | 25609211 PERFORM |
| 17 | Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia | Dang-Vu TT; Hatch B; Salimi A; Mograss M; Boucetta S; O' Byrne J; Brandewinder M; Berthomier C; Gouin JP; | 29157588 PERFORM |
| Title: | Sound degradation type differentially affects neural indicators of cognitive workload and speech tracking | ||||
| Authors: | Gagné N, Greenlaw KM, Coffey EBJ | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/40412301/ | ||||
| DOI: | 10.1016/j.heares.2025.109303 | ||||
| Publication: | Hearing research | ||||
| Keywords: | Alpha band; Cognitive workload; Electroencephalography; Hearing-in-noise; Neural speech tracking; Speech perception; Theta band; | ||||
| PMID: | 40412301 | Category: | Date Added: | 2025-05-25 | |
| Dept Affiliation: |
PSYCHOLOGY
1 Department of Psychology, Concordia University, Montréal, Canada; International Laboratory for Brain, Music and Sound Research (BRAMS); The Centre for Research on Brain, Language and Music (CRBLM). Electronic address: nathan.gagne@mail.concordia.ca. 2 Department of Psychology, Concordia University, Montréal, Canada; International Laboratory for Brain, Music and Sound Research (BRAMS); The Centre for Research on Brain, Language and Music (CRBLM). |
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Description: |
Hearing-in-noise (HIN) is a challenging task that is essential to human functioning in social, vocational, and educational contexts. Successful speech perception in noisy settings is thought to rely in part on the brain's ability to enhance neural representations of attended speech. In everyday HIN situations, important features of speech (i.e., pitch, rhythm) may be degraded in addition to being embedded in noise. The impact of these differences in sound quality on experiences of workload and neural representations of speech will be important for informing our knowledge on the cognitive demands imposed by every-day difficult listening situations. We investigated HIN perception in 20 healthy adults using continuous speech that was either clean, spectrally degraded, or temporally degraded. Each sound condition was presented both with and without pink noise. Participants engaged in a selective listening task, in which a short-story was presented with varying sound quality, while EEG data were recorded. Neural correlates of cognitive workload were obtained using power levels of two frequency bands sensitive to task difficulty manipulations: alpha (8 - 12 Hz) and theta (4 - 8 Hz). Acoustic and linguistic features (speech envelope, word onsets, word surprisal) were decoded to reveal the degree to which speech was successfully encoded. Overall, alpha-theta power increased significantly when noise was added across sound conditions, while prediction accuracy of speech tracking decreased, suggesting that more effort was required to listen, and that the speech was not as successfully encoded. The temporal degradation also resulted in greater EEG power, possibly as a function of a compensatory mechanism to restore the important temporal information required for speech comprehension. Our findings suggest that measures related to cognitive workload and successful speech encoding are differentially affected by noise and sound degradations, which may help to inform future interventions that aim to mitigate these every-day challenges. |



