@InProceedings{8553130,
  author = {R. Anderson and M. Sandsten},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Classification of EEG signals based on mean-square error optimal time-frequency features},
  year = {2018},
  pages = {106-110},
  abstract = {This paper illustrates the improvement in accuracy of classification for electroencephalogram (EEG) signals measured during a memory encoding task, by using features based on a mean square error (MSE) optimal time-frequency estimator. The EEG signals are modelled as Locally Stationary Processes, based on the modulation in time of an ordinary stationary covariance function. After estimating the model parameters, we compute the MSE optimal kernel for the estimation of the Wigner-Ville spectrum. We present a simulation study to evaluate the performance of the derived optimal spectral estimator, compared to the single windowed Hanning spectrogram and the Welch spectrogram. Further, the estimation procedure is applied to the measured EEG and the time-frequency features extracted from the spectral estimates are used to feed a neural network classifier. Consistent improvement in classification accuracy is obtained by using the features from the proposed estimator, compared to the use of existing methods.},
  keywords = {covariance analysis;electroencephalography;feature extraction;mean square error methods;medical signal processing;neural nets;signal classification;spectral analysis;time-frequency analysis;Wigner distribution;mean-square error optimal time-frequency features;electroencephalogram signals;memory encoding task;ordinary stationary covariance function;MSE optimal kernel;single windowed Hanning spectrogram;classification accuracy;neural network classifier;time-frequency feature extraction;Welch spectrogram;Wigner-Ville spectrum estimation;spectral estimator;locally stationary processes;EEG signal classification;Time-frequency analysis;Brain modeling;Electroencephalography;Kernel;Computational modeling;Feature extraction;Spectrogram},
  doi = {10.23919/EUSIPCO.2018.8553130},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437640.pdf},
}

{"_id":"bA7dBL8QQfvn6iuaY","bibbaseid":"anderson-sandsten-classificationofeegsignalsbasedonmeansquareerroroptimaltimefrequencyfeatures-2018","authorIDs":[],"author_short":["Anderson, R.","Sandsten, M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["R."],"propositions":[],"lastnames":["Anderson"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Sandsten"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Classification of EEG signals based on mean-square error optimal time-frequency features","year":"2018","pages":"106-110","abstract":"This paper illustrates the improvement in accuracy of classification for electroencephalogram (EEG) signals measured during a memory encoding task, by using features based on a mean square error (MSE) optimal time-frequency estimator. The EEG signals are modelled as Locally Stationary Processes, based on the modulation in time of an ordinary stationary covariance function. After estimating the model parameters, we compute the MSE optimal kernel for the estimation of the Wigner-Ville spectrum. We present a simulation study to evaluate the performance of the derived optimal spectral estimator, compared to the single windowed Hanning spectrogram and the Welch spectrogram. Further, the estimation procedure is applied to the measured EEG and the time-frequency features extracted from the spectral estimates are used to feed a neural network classifier. Consistent improvement in classification accuracy is obtained by using the features from the proposed estimator, compared to the use of existing methods.","keywords":"covariance analysis;electroencephalography;feature extraction;mean square error methods;medical signal processing;neural nets;signal classification;spectral analysis;time-frequency analysis;Wigner distribution;mean-square error optimal time-frequency features;electroencephalogram signals;memory encoding task;ordinary stationary covariance function;MSE optimal kernel;single windowed Hanning spectrogram;classification accuracy;neural network classifier;time-frequency feature extraction;Welch spectrogram;Wigner-Ville spectrum estimation;spectral estimator;locally stationary processes;EEG signal classification;Time-frequency analysis;Brain modeling;Electroencephalography;Kernel;Computational modeling;Feature extraction;Spectrogram","doi":"10.23919/EUSIPCO.2018.8553130","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437640.pdf","bibtex":"@InProceedings{8553130,\n author = {R. Anderson and M. Sandsten},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Classification of EEG signals based on mean-square error optimal time-frequency features},\n year = {2018},\n pages = {106-110},\n abstract = {This paper illustrates the improvement in accuracy of classification for electroencephalogram (EEG) signals measured during a memory encoding task, by using features based on a mean square error (MSE) optimal time-frequency estimator. The EEG signals are modelled as Locally Stationary Processes, based on the modulation in time of an ordinary stationary covariance function. After estimating the model parameters, we compute the MSE optimal kernel for the estimation of the Wigner-Ville spectrum. We present a simulation study to evaluate the performance of the derived optimal spectral estimator, compared to the single windowed Hanning spectrogram and the Welch spectrogram. Further, the estimation procedure is applied to the measured EEG and the time-frequency features extracted from the spectral estimates are used to feed a neural network classifier. Consistent improvement in classification accuracy is obtained by using the features from the proposed estimator, compared to the use of existing methods.},\n keywords = {covariance analysis;electroencephalography;feature extraction;mean square error methods;medical signal processing;neural nets;signal classification;spectral analysis;time-frequency analysis;Wigner distribution;mean-square error optimal time-frequency features;electroencephalogram signals;memory encoding task;ordinary stationary covariance function;MSE optimal kernel;single windowed Hanning spectrogram;classification accuracy;neural network classifier;time-frequency feature extraction;Welch spectrogram;Wigner-Ville spectrum estimation;spectral estimator;locally stationary processes;EEG signal classification;Time-frequency analysis;Brain modeling;Electroencephalography;Kernel;Computational modeling;Feature extraction;Spectrogram},\n doi = {10.23919/EUSIPCO.2018.8553130},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437640.pdf},\n}\n\n","author_short":["Anderson, R.","Sandsten, M."],"key":"8553130","id":"8553130","bibbaseid":"anderson-sandsten-classificationofeegsignalsbasedonmeansquareerroroptimaltimefrequencyfeatures-2018","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437640.pdf"},"keyword":["covariance analysis;electroencephalography;feature extraction;mean square error methods;medical signal processing;neural nets;signal classification;spectral analysis;time-frequency analysis;Wigner distribution;mean-square error optimal time-frequency features;electroencephalogram signals;memory encoding task;ordinary stationary covariance function;MSE optimal kernel;single windowed Hanning spectrogram;classification accuracy;neural network classifier;time-frequency feature extraction;Welch spectrogram;Wigner-Ville spectrum estimation;spectral estimator;locally stationary processes;EEG signal classification;Time-frequency analysis;Brain modeling;Electroencephalography;Kernel;Computational modeling;Feature extraction;Spectrogram"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2018url.bib","creationDate":"2021-02-13T15:38:40.143Z","downloads":0,"keywords":["covariance analysis;electroencephalography;feature extraction;mean square error methods;medical signal processing;neural nets;signal classification;spectral analysis;time-frequency analysis;wigner distribution;mean-square error optimal time-frequency features;electroencephalogram signals;memory encoding task;ordinary stationary covariance function;mse optimal kernel;single windowed hanning spectrogram;classification accuracy;neural network classifier;time-frequency feature extraction;welch spectrogram;wigner-ville spectrum estimation;spectral estimator;locally stationary processes;eeg signal classification;time-frequency analysis;brain modeling;electroencephalography;kernel;computational modeling;feature extraction;spectrogram"],"search_terms":["classification","eeg","signals","based","mean","square","error","optimal","time","frequency","features","anderson","sandsten"],"title":"Classification of EEG signals based on mean-square error optimal time-frequency features","year":2018,"dataSources":["yiZioZximP7hphDpY","iuBeKSmaES2fHcEE9"]}