{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:05:09Z","timestamp":1781071509650,"version":"3.54.1"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:p>\n                    This study developed a novel, fully reproducible EEG-based framework for binary emotion recognition that combines phase-space reconstruction with Poincar\u00e9 sections to capture the nonlinear dynamics of brain activity during prototypical emotional states. The method was applied to the publicly available AMIGOS dataset. EEG recordings from 33 participants were downsampled to 128\u202fHz, bandpass-filtered (4\u201345\u202fHz), cleaned of ocular and muscular artifacts using independent component analysis (ICA), and segmented into 1-s non-overlapping windows. Strict labeling thresholds (valence \u2265 6 and arousal \u2265 6 for Happy; valence \u2264 4 and arousal \u2264 4 for Sad) were enforced to isolate extreme high-valence\/high-arousal (HVHA) versus low-valence\/low-arousal (LVLA) states. A hybrid feature set integrating Poincar\u00e9-derived geometric measures with classical spectral power and frontal asymmetry indices underwent rigorous two-stage selection. The final support vector machine with radial basis function kernel (SVM-RBF) achieved 98.21\u202f\u00b1\u202f0.54% accuracy, 96.42\u202f\u00b1\u202f1.06% sensitivity, and 100% specificity in strict subject-independent 7-fold cross-validation. Symmetric selection of 14 channels significantly enhanced feature separability (paired Wilcoxon signed-rank test, Bonferroni-corrected\n                    <jats:italic>p<\/jats:italic>\n                    \u202f=\u202f7.4\u202f\u00d7\u202f10\n                    <jats:sup>\u22128<\/jats:sup>\n                    ). Independent validation on the DEAP dataset using the identical pipeline yielded 97.68% accuracy, confirming generalizability. The near-perfect performance is specific to binary classification of extreme affective quadrants and does not extend to standard 4-class tasks (81.7%). These findings demonstrate the physiological relevance of nonlinear geometric analysis for detecting prototypical joy versus sadness, with potential clinical utility in automated depression screening.\n                  <\/jats:p>","DOI":"10.3389\/fnhum.2026.1660402","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T05:39:30Z","timestamp":1781069970000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["EEG-based emotion recognition using phase-space reconstruction with Poincar\u00e9 sections: a study on the AMIGOS dataset"],"prefix":"10.3389","volume":"20","author":[{"given":"Mahnam","family":"Mirzaee","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Ma.C., Islamic Azad University","place":["Mashhad, Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Azarnoosh","sequence":"additional","affiliation":[{"name":"Research Center of Biomedical Engineering, Ma.C., Islamic Azad University","place":["Mashhad, Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid Reza","family":"Kobravi","sequence":"additional","affiliation":[{"name":"Research Center of Biomedical Engineering, Ma.C., Islamic Azad University","place":["Mashhad, Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"108078","DOI":"10.1016\/j.apacoust.2021.108078","article-title":"Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features","volume":"179","author":"Akbari","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref2","first-page":"213","volume-title":"International Work-Conference on the Interplay between Natural and Artificial Computation","author":"Alcaraz","year":"2017"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neuroscience.2019.10.034","article-title":"K-complexes detection in EEG signals using fractal and frequency features coupled with an ensemble classification model","volume":"422","author":"AL-Salman","year":"2019","journal-title":"Neuroscience"},{"key":"ref9003","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.3390\/app7121239","article-title":"Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review","volume":"7","author":"Al-Nafjan","year":"2017","journal-title":"Appl. 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Rep."},{"key":"ref6","first-page":"1","volume-title":"In 2023 3rd International Conference on Intelligent Technologies (CONIT)","author":"Bansal","year":"2023"},{"key":"ref9010","doi-asserted-by":"publisher","first-page":"105875","DOI":"10.1016\/j.bspc.2023.105875","article-title":"A subject-independent portable emotion recognition system using synchrosqueezing wavelet transform maps of EEG signals and ResNet-18","volume":"90","author":"Bagherzadeh","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1272834","DOI":"10.3389\/fnins.2023.1272834","article-title":"Multi-feature fusion learning for Alzheimer\u2019s disease prediction using EEG signals in resting state","volume":"17","author":"Chen","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref9004","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.biopsycho.2004.03.008","article-title":"What does the prefrontal cortex \u201cdo\u201d in affect: perspectives on frontal EEG asymmetry research","volume":"67","author":"Davidson","year":"2004","journal-title":"Biol. Psychol."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1006\/nimg.2000.0538","article-title":"Dissociable temporal lobe activations during emotional episodic memory retrieval","volume":"11","author":"Dolan","year":"2000","journal-title":"NeuroImage"},{"key":"ref10","first-page":"81","author":"Duan","year":"2013"},{"key":"ref9014","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1007\/s10044-025-01501-1","article-title":"Subject-independent multi-channel voting for EEG-based emotion recognition using wavelet scattering deep network and advanced signal metrics","volume":"28","author":"Elrefaiy","year":"2025","journal-title":"Pattern. Anal. Applic."},{"key":"ref9005","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1103\/PhysRevA.33.1134","article-title":"Independent coordinates for strange attractors from mutual information","volume":"33","author":"Fraser","year":"1986","journal-title":"Phys. Rev. A"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"S487","DOI":"10.1016\/j.euroneuro.2012.07.008","article-title":"Neural correlates of emotion processing: from emotional to social brain","volume":"22","author":"Fossati","year":"2012","journal-title":"Eur. Neuropsychopharmacol."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"7071","DOI":"10.1038\/s41598-021-86345-5","article-title":"Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification","volume":"11","author":"Gannouni","year":"2021","journal-title":"Sci. Rep."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1108\/ACI-05-2021-0130","article-title":"An overlapping sliding window and combined features based emotion recognition system for EEG signals","volume":"21","author":"Garg","year":"2021","journal-title":"Appl. Comput. Informat."},{"key":"ref15","author":"Gargano","year":"2024"},{"key":"ref16","first-page":"353","article-title":"Combination of sLORETA and nonlinear coupling for emotional EEG source localization","volume":"20","author":"Goshvarpour","year":"2016","journal-title":"Nonlinear Dynamics Psychol. Life Sci."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s11571-021-09735-5","article-title":"Innovative Poincar\u00e9\u2019s plot asymmetry descriptors for EEG emotion recognition","volume":"16","author":"Goshvarpour","year":"2022","journal-title":"Cogn. Neurodyn."},{"key":"ref9006","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.biopsycho.2009.08.010","article-title":"The role of asymmetric frontal cortical activity in emotion-related phenomena: a review and update","volume":"84","author":"Harmon-Jones","year":"2010","journal-title":"Biol. Psychol."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: a database for emotion analysis; using physiological signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref9001","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1515\/bmt-2019-0121","article-title":"A nonlinear dynamical approach to analysis of emotions using EEG signals based on the Poincar\u00e9 map function and recurrence plots. Biomed. Eng.\/biomed","volume":"65","author":"Khodabakhshi","year":"2020","journal-title":"Tech."},{"key":"ref9002","volume-title":"Visualization of Seizure Activity Using Peak-to-Peak Intervals","author":"Khosravani","year":"2015"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"13291","DOI":"10.1007\/s00521-022-06942-x","article-title":"Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet","volume":"34","author":"Kumari","year":"2022","journal-title":"Neural Comput. & Applic."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s10916-019-1517-9","article-title":"Multi-feature fusion method based on EEG signal and its application in stroke classification","volume":"44","author":"Li","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"7677","DOI":"10.3390\/app10217677","article-title":"Maximum marginal approach on EEG signal preprocessing for emotion detection","volume":"10","author":"Li","year":"2020","journal-title":"Appl. Sci. (Switz.)"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1517273","DOI":"10.3389\/fnhum.2025.1517273","article-title":"A spatial and temporal transformer-based EEG emotion recognition in VR environment","volume":"19","author":"Li","year":"2025","journal-title":"Front. Hum. Neurosci."},{"key":"ref23","first-page":"896","volume-title":"In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Liu","year":"2018"},{"key":"ref24","author":"Martinez-Tejada","year":"2020"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1212\/WNL.60.3.426","article-title":"Impaired facial emotion recognition in early-onset right mesial temporal lobe epilepsy","volume":"60","author":"Meletti","year":"2003","journal-title":"Neurology"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s40708-022-00162-8","article-title":"Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata","volume":"9","author":"Menon","year":"2022","journal-title":"Brain Informat."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1109\/TAFFC.2018.2884461","article-title":"AMIGOS: a dataset for affect, personality and mood research on individuals and groups","volume":"12","author":"Miranda-Correa","year":"2018","journal-title":"IEEE Trans. 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J."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"3248","DOI":"10.3390\/s22093248","article-title":"Emotion recognition using a reduced set of EEG channels based on holographic feature maps","volume":"22","author":"Topic","year":"2022","journal-title":"Sensors"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.3390\/app11031338","article-title":"Multidimensional emotion recognition based on semantic analysis of biomedical EEG signal for knowledge discovery in psychological healthcare","volume":"11","author":"Wang","year":"2021","journal-title":"Appl. Sci. 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