@InProceedings{8553014,
  author = {A. S. Janani and T. S. Grummett and H. Bakhshayesh and J. O. Willoughby and K. J. Pope},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Beamforming and Blind Source Separation Have a Complementary Effect in Reducing Tonic Cranial Muscle Contamination of Scalp Measurements},
  year = {2018},
  pages = {86-90},
  abstract = {Scalp electroencephalograms (EEG) are susceptible to cranial and cervical muscle contamination from frequencies as low as 20 hertz, even in relaxed conditions. Reliably recording cognitive activity, which is in this range, is impossible without removing or reducing the effect of muscle contamination. Our unique database of paralysed conscious subjects enabled us to test the effect of combining beamforming and blind source separation in reducing tonic muscle contamination of scalp electrical recordings. Using the beamforming technique, muscle sources are separated automatically based on their location; while using blind source separation, muscle components are separated based on their spectral gradient. Our results show that applying the beamforming technique on data pruned by a blind source separation technique (or vice versa) can reduce tonic muscle contamination significantly more than applying either of them separately, especially at peripheral locations. Hence, these approaches complement each other in reducing muscle contamination of EEG.},
  keywords = {array signal processing;blind source separation;cognition;electroencephalography;medical signal processing;muscle;neurophysiology;tonic muscle contamination;scalp electrical recordings;beamforming technique;muscle sources;muscle components;blind source separation technique;complementary effect;tonic cranial muscle contamination;scalp measurements;scalp electroencephalograms;cervical muscle contamination;EEG;peripheral locations;spectral gradient;paralysed conscious subjects;cognitive activity;frequency 20.0 Hz;Muscles;Electroencephalography;Contamination;Array signal processing;Scalp;Electromyography;Task analysis;beamforming;blind source separation;muscle contamination;electroencephalograph;neurophysiological response},
  doi = {10.23919/EUSIPCO.2018.8553014},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570435121.pdf},
}

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J."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["A.","S."],"propositions":[],"lastnames":["Janani"],"suffixes":[]},{"firstnames":["T.","S."],"propositions":[],"lastnames":["Grummett"],"suffixes":[]},{"firstnames":["H."],"propositions":[],"lastnames":["Bakhshayesh"],"suffixes":[]},{"firstnames":["J.","O."],"propositions":[],"lastnames":["Willoughby"],"suffixes":[]},{"firstnames":["K.","J."],"propositions":[],"lastnames":["Pope"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Beamforming and Blind Source Separation Have a Complementary Effect in Reducing Tonic Cranial Muscle Contamination of Scalp Measurements","year":"2018","pages":"86-90","abstract":"Scalp electroencephalograms (EEG) are susceptible to cranial and cervical muscle contamination from frequencies as low as 20 hertz, even in relaxed conditions. Reliably recording cognitive activity, which is in this range, is impossible without removing or reducing the effect of muscle contamination. Our unique database of paralysed conscious subjects enabled us to test the effect of combining beamforming and blind source separation in reducing tonic muscle contamination of scalp electrical recordings. Using the beamforming technique, muscle sources are separated automatically based on their location; while using blind source separation, muscle components are separated based on their spectral gradient. Our results show that applying the beamforming technique on data pruned by a blind source separation technique (or vice versa) can reduce tonic muscle contamination significantly more than applying either of them separately, especially at peripheral locations. Hence, these approaches complement each other in reducing muscle contamination of EEG.","keywords":"array signal processing;blind source separation;cognition;electroencephalography;medical signal processing;muscle;neurophysiology;tonic muscle contamination;scalp electrical recordings;beamforming technique;muscle sources;muscle components;blind source separation technique;complementary effect;tonic cranial muscle contamination;scalp measurements;scalp electroencephalograms;cervical muscle contamination;EEG;peripheral locations;spectral gradient;paralysed conscious subjects;cognitive activity;frequency 20.0 Hz;Muscles;Electroencephalography;Contamination;Array signal processing;Scalp;Electromyography;Task analysis;beamforming;blind source separation;muscle contamination;electroencephalograph;neurophysiological response","doi":"10.23919/EUSIPCO.2018.8553014","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570435121.pdf","bibtex":"@InProceedings{8553014,\n author = {A. S. Janani and T. S. Grummett and H. Bakhshayesh and J. O. Willoughby and K. J. Pope},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Beamforming and Blind Source Separation Have a Complementary Effect in Reducing Tonic Cranial Muscle Contamination of Scalp Measurements},\n year = {2018},\n pages = {86-90},\n abstract = {Scalp electroencephalograms (EEG) are susceptible to cranial and cervical muscle contamination from frequencies as low as 20 hertz, even in relaxed conditions. Reliably recording cognitive activity, which is in this range, is impossible without removing or reducing the effect of muscle contamination. Our unique database of paralysed conscious subjects enabled us to test the effect of combining beamforming and blind source separation in reducing tonic muscle contamination of scalp electrical recordings. Using the beamforming technique, muscle sources are separated automatically based on their location; while using blind source separation, muscle components are separated based on their spectral gradient. Our results show that applying the beamforming technique on data pruned by a blind source separation technique (or vice versa) can reduce tonic muscle contamination significantly more than applying either of them separately, especially at peripheral locations. 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