{"id":21270194,"url":"https://github.com/raghav714/eeg-emotion-classification","last_synced_at":"2025-10-06T01:25:07.892Z","repository":{"id":226796324,"uuid":"140014612","full_name":"Raghav714/EEG-Emotion-classification","owner":"Raghav714","description":null,"archived":false,"fork":false,"pushed_at":"2019-03-16T05:04:25.000Z","size":4067,"stargazers_count":96,"open_issues_count":0,"forks_count":35,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-03-09T18:44:16.762Z","etag":null,"topics":["eeg-classification","eeg-data","eeg-signals-processing","emotion-recognition"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Raghav714.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2018-07-06T17:45:31.000Z","updated_at":"2024-03-09T18:44:23.348Z","dependencies_parsed_at":"2024-03-09T18:54:23.798Z","dependency_job_id":null,"html_url":"https://github.com/Raghav714/EEG-Emotion-classification","commit_stats":null,"previous_names":["raghav714/eeg-emotion-classification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Raghav714%2FEEG-Emotion-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Raghav714%2FEEG-Emotion-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Raghav714%2FEEG-Emotion-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Raghav714%2FEEG-Emotion-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Raghav714","download_url":"https://codeload.github.com/Raghav714/EEG-Emotion-classification/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225693724,"owners_count":17509227,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["eeg-classification","eeg-data","eeg-signals-processing","emotion-recognition"],"created_at":"2024-11-21T08:16:07.180Z","updated_at":"2025-10-06T01:25:02.856Z","avatar_url":"https://github.com/Raghav714.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# EEG-Emotion-classification\n\n# PROBLEM S TATEMENT\nIt is difficult to look at the EEG signal and identify the state of Human mind. In this assign-\nment, the SVM classifier is trained with Deap dataset to predict the state of mind. the state of\nmind is predicted in terms of valence, arousal. which can further be used to predict the state\nof mind in terms of expression.\n# PROCEDURE TO SOLVE THE ABOVE PROBLEM\nIn this assignment, the preprocessed data is used for training the classifier.\nSteps involve in training the dataset:-\n1. Extracting the dataset\n2. Finding the features\n3. Reducing the dimension\n4. traning the vector\n5. checking the classifier efficiency\n## EXTRACTING THE DATASET\nThe DEAP dataset consists of two parts:\n1. The ratings from an online self-assessment where 120 one-minute extracts of music\nvideos were each rated by 14-16 volunteers based on arousal, valence and dominance.\n2. The participant ratings, physiological recordings and face video of an experiment where\n32 volunteers watched a subset of 40 of the above music videos. EEG and physiological\nsignals were recorded and each participant also rated the videos as above.\nIn this assignment, labels are extracted into separate file and data of each channel is extracted\ninto separate file. data from each channel is stored in row wise versus time in column for each\ntrail,per person\n## FINDING THE FEATURES\nIn this assignment, Wavelet transform is used to decompose the each channel data into the\nfive feature i.e\n• Delta (\u003c 4 Hz)\n• Theta (4-7 Hz)\n• Alpha (8-15 Hz)\n• Beta (16-31 Hz)\n• Gamma (\u003e 32 Hz)\nIn this assignment, obtained the 7 decomposed values but we negalted the frequency whose\nrange is in 0-0.5 Hz so that the artifcats are removed. The frequency whose range is near 50\nHz are removed to reduce the effect of power line on signals. finally, EEG band are obtained\nfor each channel.\n## REDUCING THE DIMENSION\nThe dimension can be reduced using one of the below mention method:-\n1. Standard Deviation\n2. Mean\n3. Variance\n4. Median\nBut in this assignment Standard Deviation is used because it describe the devaition of each\nEEG Band power density properly given by the equation below.\n## TRANING THE VECTOR\nIn this assignment, the classifier used is Support vector machine (SVM). we can also use other\nclassifier or neural network to predict the values but the training efficiency is found to be\nnearly 98 percentage with SVM.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraghav714%2Feeg-emotion-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraghav714%2Feeg-emotion-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraghav714%2Feeg-emotion-classification/lists"}