Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features
IntroductionPhase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movem...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1521491/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850069156053909504 |
|---|---|
| author | Morgane Marzulli Alexandre Bleuzé Joe Saad Felix Martel Philippe Ciuciu Philippe Ciuciu Tetiana Aksenova Lucas Struber |
| author_facet | Morgane Marzulli Alexandre Bleuzé Joe Saad Felix Martel Philippe Ciuciu Philippe Ciuciu Tetiana Aksenova Lucas Struber |
| author_sort | Morgane Marzulli |
| collection | DOAJ |
| description | IntroductionPhase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.MethodsThis study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.ResultsThe PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.DiscussionThese preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems. |
| format | Article |
| id | doaj-art-7c3e2f5ef8fe42b8a7e1c286cc4af316 |
| institution | DOAJ |
| issn | 1662-5161 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj-art-7c3e2f5ef8fe42b8a7e1c286cc4af3162025-08-20T02:47:50ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612025-03-011910.3389/fnhum.2025.15214911521491Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling featuresMorgane Marzulli0Alexandre Bleuzé1Joe Saad2Felix Martel3Philippe Ciuciu4Philippe Ciuciu5Tetiana Aksenova6Lucas Struber7Clinatec, CEA, LETI, University Grenoble Alpes, Grenoble, FranceClinatec, CEA, LETI, University Grenoble Alpes, Grenoble, FranceCEA, LIST, University Grenoble Alpes, Grenoble, FranceClinatec, CEA, LETI, University Grenoble Alpes, Grenoble, FranceCEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, FranceMIND Team, Inria, Université Paris-Saclay, Palaiseau, FranceClinatec, CEA, LETI, University Grenoble Alpes, Grenoble, FranceClinatec, CEA, LETI, University Grenoble Alpes, Grenoble, FranceIntroductionPhase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.MethodsThis study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.ResultsThe PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.DiscussionThese preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.https://www.frontiersin.org/articles/10.3389/fnhum.2025.1521491/fullbrain-computer interfaceelectrocorticographymotor decodingneural featuresphase-amplitude coupling |
| spellingShingle | Morgane Marzulli Alexandre Bleuzé Joe Saad Felix Martel Philippe Ciuciu Philippe Ciuciu Tetiana Aksenova Lucas Struber Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features Frontiers in Human Neuroscience brain-computer interface electrocorticography motor decoding neural features phase-amplitude coupling |
| title | Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features |
| title_full | Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features |
| title_fullStr | Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features |
| title_full_unstemmed | Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features |
| title_short | Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features |
| title_sort | classifying mental motor tasks from chronic ecog bci recordings using phase amplitude coupling features |
| topic | brain-computer interface electrocorticography motor decoding neural features phase-amplitude coupling |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1521491/full |
| work_keys_str_mv | AT morganemarzulli classifyingmentalmotortasksfromchronicecogbcirecordingsusingphaseamplitudecouplingfeatures AT alexandrebleuze classifyingmentalmotortasksfromchronicecogbcirecordingsusingphaseamplitudecouplingfeatures AT joesaad classifyingmentalmotortasksfromchronicecogbcirecordingsusingphaseamplitudecouplingfeatures AT felixmartel classifyingmentalmotortasksfromchronicecogbcirecordingsusingphaseamplitudecouplingfeatures AT philippeciuciu classifyingmentalmotortasksfromchronicecogbcirecordingsusingphaseamplitudecouplingfeatures AT philippeciuciu classifyingmentalmotortasksfromchronicecogbcirecordingsusingphaseamplitudecouplingfeatures AT tetianaaksenova classifyingmentalmotortasksfromchronicecogbcirecordingsusingphaseamplitudecouplingfeatures AT lucasstruber classifyingmentalmotortasksfromchronicecogbcirecordingsusingphaseamplitudecouplingfeatures |