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Academic Journal

Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques.

Authors : Raeisi Z; Department of Computer Science, University of Fairleigh Dickinson, Vancouver Campus, Vancouver, Canada.; Sodagartojgi A

Subjects: Tinnitus*/Tinnitus*/Tinnitus*/classification ; Tinnitus*/Tinnitus*/Tinnitus*/physiopathology ; Tinnitus*/Tinnitus*/Tinnitus*/diagnosis

Source: Scientific reports [Sci Rep] 2025 May 07; Vol. 15 (1), pp. 15959. Date of Electronic Publication: 2025 May 07.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.

Authors : Nayana BR; Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.; Pavithra MN

Subjects: Deep Learning* ; Electroencephalography*/Electroencephalography*/Electroencephalography*/methods ; Neurodegenerative Diseases*/Neurodegenerative Diseases*/Neurodegenerative Diseases*/diagnosis

Source: Scientific reports [Sci Rep] 2025 May 07; Vol. 15 (1), pp. 15950. Date of Electronic Publication: 2025 May 07.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Detection and location of EEG events using deep learning visual inspection.

Authors : Fraiwan MA; Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan.

Subjects: Electroencephalography*/Electroencephalography*/Electroencephalography*/methods ; Deep Learning*; Humans

Source: PloS one [PLoS One] 2024 Dec 23; Vol. 19 (12), pp. e0312763. Date of Electronic Publication: 2024 Dec 23 (Print Publication: 2024).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.

Authors : Daly A; Department of Mathematics, Munster Technological University, Cork, Ireland. .; Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland. .

Subjects: Deep Learning* ; Electroencephalography*/Electroencephalography*/Electroencephalography*/methods; Humans

Source: Scientific reports [Sci Rep] 2024 Nov 14; Vol. 14 (1), pp. 28059. Date of Electronic Publication: 2024 Nov 14.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.

Authors : Rehman M; Institute of Computer Science, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan.; Anwer H

Subjects: Electroencephalography*/Electroencephalography*/Electroencephalography*/methods ; Deep Learning* ; Brain*/Brain*/Brain*/physiology

Source: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Oct 30; Vol. 24 (21). Date of Electronic Publication: 2024 Oct 30.Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220

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Academic Journal

AnEEG: leveraging deep learning for effective artifact removal in EEG data.

Authors : Kalita B; Department of Information Technology, Gauhati University, Guwahati, Assam, 781014, India.; Deb N

Subjects: Electroencephalography*/Electroencephalography*/Electroencephalography*/methods ; Deep Learning* ; Artifacts*

Source: Scientific reports [Sci Rep] 2024 Oct 16; Vol. 14 (1), pp. 24234. Date of Electronic Publication: 2024 Oct 16.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.

Authors : Alsuradi H; Engineering Division, New York University Abu Dhabi, Abu Dhabi, UAE.; Center for Artificial Intelligence and Robotics, New York University Abu Dhabi, Abu Dhabi, UAE.

Subjects: Deep Learning* ; Electroencephalography*/Electroencephalography*/Electroencephalography*/methods ; Vibration*

Source: Scientific reports [Sci Rep] 2024 Aug 23; Vol. 14 (1), pp. 19604. Date of Electronic Publication: 2024 Aug 23.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting.

Authors : Dar MN; National University of Sciences and Technology, Islamabad, 44000, Pakistan. .; Akram MU

Subjects: Electroencephalography*/Electroencephalography*/Electroencephalography*/methods ; Emotions*/Emotions*/Emotions*/physiology ; Deep Learning*

Source: Scientific reports [Sci Rep] 2024 Jul 24; Vol. 14 (1), pp. 17080. Date of Electronic Publication: 2024 Jul 24.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

A hybrid EEG classification model using layered cascade deep learning architecture.

Authors : Liu C; College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China.; Chen W

Subjects: Electroencephalography*/Electroencephalography*/Electroencephalography*/methods ; Deep Learning* ; Seizures*/Seizures*/Seizures*/diagnosis

Source: Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Jul; Vol. 62 (7), pp. 2213-2229. Date of Electronic Publication: 2024 Mar 20.Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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Academic Journal

A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.

Authors : He L; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, 400044, People's Republic of China.; Zhang L

Subjects: Deep Learning* ; Electroencephalography*/Electroencephalography*/Electroencephalography*/methods ; Fatigue*/Fatigue*/Fatigue*/diagnosis

Source: Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Jun; Vol. 62 (6), pp. 1781-1793. Date of Electronic Publication: 2024 Feb 20.Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques.

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.

Detection and location of EEG events using deep learning visual inspection.

Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.

AnEEG: leveraging deep learning for effective artifact removal in EEG data.

Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting.

A hybrid EEG classification model using layered cascade deep learning architecture.

A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.

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