{"_id":"ihNwpF6hxcnCKDaas","bibbaseid":"shahtalebi-mohammadi-errorcorrectionoutputcoddingcoupledwiththecspformotorimagerybcisystems-2017","authorIDs":[],"author_short":["Shahtalebi, S.","Mohammadi, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["S."],"propositions":[],"lastnames":["Shahtalebi"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Mohammadi"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"Error correction output codding coupled with the CSP for motor imagery BCI systems","year":"2017","pages":"2071-2075","abstract":"Motivated by the fact that modeling and representation of multi-class signal patterns plays a critical role in Electroencephalogram (EEG)-based brain computer interface (BCI) systems, the paper proposes the coupling of error correction output coding (ECOC) with the common spatial pattern (CSP) analysis. Referred to as the ECO-CSP framework, the ECOC approach is applied to EEG motor imagery classification problem. A BCI system designed to operate in real world conditions, must be able to discriminate multiple tasks and activities. This fact, expresses the urge to develop/implement classifiers intrinsically designed for multi-class problems. One of such techniques which is well regarded in other fields but has not yet been applied to EEG-based classification is the ECOC. The paper addresses this gap. The BCI Competition IV-2a dataset is used to evaluate the performance of the proposed ECO-CSP framework. Our results show that ECO-CSP achieve similar performance in comparison to the state-of-the-art algorithms but is extensively simpler with significantly less computational overhead making it a practical alternative for real-time EEG motor imagery classification tasks.","keywords":"brain-computer interfaces;electroencephalography;error correction codes;medical signal processing;signal classification;brain computer interface systems;error correction output coding;common spatial pattern analysis;ECO-CSP framework;ECOC approach;EEG motor imagery classification problem;multiclass problems;BCI Competition IV-2a dataset;EEG motor imagery classification tasks;motor imagery BCI systems;multiclass signal patterns;electroencephalogram;Electroencephalography;Feature extraction;Covariance matrices;Brain;Encoding;Signal processing;Brain-computer interface (BCI);Common spatial patterns;Electroencephalogram (EEG);Error correction output coding;Motor Imagery","doi":"10.23919/EUSIPCO.2017.8081574","issn":"2076-1465","month":"Aug","bibtex":"@InProceedings{8081574,\n author = {S. Shahtalebi and A. Mohammadi},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Error correction output codding coupled with the CSP for motor imagery BCI systems},\n year = {2017},\n pages = {2071-2075},\n abstract = {Motivated by the fact that modeling and representation of multi-class signal patterns plays a critical role in Electroencephalogram (EEG)-based brain computer interface (BCI) systems, the paper proposes the coupling of error correction output coding (ECOC) with the common spatial pattern (CSP) analysis. Referred to as the ECO-CSP framework, the ECOC approach is applied to EEG motor imagery classification problem. A BCI system designed to operate in real world conditions, must be able to discriminate multiple tasks and activities. This fact, expresses the urge to develop/implement classifiers intrinsically designed for multi-class problems. One of such techniques which is well regarded in other fields but has not yet been applied to EEG-based classification is the ECOC. The paper addresses this gap. The BCI Competition IV-2a dataset is used to evaluate the performance of the proposed ECO-CSP framework. Our results show that ECO-CSP achieve similar performance in comparison to the state-of-the-art algorithms but is extensively simpler with significantly less computational overhead making it a practical alternative for real-time EEG motor imagery classification tasks.},\n keywords = {brain-computer interfaces;electroencephalography;error correction codes;medical signal processing;signal classification;brain computer interface systems;error correction output coding;common spatial pattern analysis;ECO-CSP framework;ECOC approach;EEG motor imagery classification problem;multiclass problems;BCI Competition IV-2a dataset;EEG motor imagery classification tasks;motor imagery BCI systems;multiclass signal patterns;electroencephalogram;Electroencephalography;Feature extraction;Covariance matrices;Brain;Encoding;Signal processing;Brain-computer interface (BCI);Common spatial patterns;Electroencephalogram (EEG);Error correction output coding;Motor Imagery},\n doi = {10.23919/EUSIPCO.2017.8081574},\n issn = {2076-1465},\n month = {Aug},\n}\n\n","author_short":["Shahtalebi, S.","Mohammadi, A."],"key":"8081574","id":"8081574","bibbaseid":"shahtalebi-mohammadi-errorcorrectionoutputcoddingcoupledwiththecspformotorimagerybcisystems-2017","role":"author","urls":{},"keyword":["brain-computer interfaces;electroencephalography;error correction codes;medical signal processing;signal classification;brain computer interface systems;error correction output coding;common spatial pattern analysis;ECO-CSP framework;ECOC approach;EEG motor imagery classification problem;multiclass problems;BCI Competition IV-2a dataset;EEG motor imagery classification tasks;motor imagery BCI systems;multiclass signal patterns;electroencephalogram;Electroencephalography;Feature extraction;Covariance matrices;Brain;Encoding;Signal processing;Brain-computer interface (BCI);Common spatial patterns;Electroencephalogram (EEG);Error correction output coding;Motor Imagery"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","creationDate":"2021-02-13T16:38:25.739Z","downloads":0,"keywords":["brain-computer interfaces;electroencephalography;error correction codes;medical signal processing;signal classification;brain computer interface systems;error correction output coding;common spatial pattern analysis;eco-csp framework;ecoc approach;eeg motor imagery classification problem;multiclass problems;bci competition iv-2a dataset;eeg motor imagery classification tasks;motor imagery bci systems;multiclass signal patterns;electroencephalogram;electroencephalography;feature extraction;covariance matrices;brain;encoding;signal processing;brain-computer interface (bci);common spatial patterns;electroencephalogram (eeg);error correction output coding;motor imagery"],"search_terms":["error","correction","output","codding","coupled","csp","motor","imagery","bci","systems","shahtalebi","mohammadi"],"title":"Error correction output codding coupled with the CSP for motor imagery BCI systems","year":2017,"dataSources":["2MNbFYjMYTD6z7ExY","uP2aT6Qs8sfZJ6s8b"]}