@InProceedings{8553165,
  author = {D. Jin and B. Boiadjieva and H. Backhaus and M. Fauss and T. Fu and A. Stroh and A. Klein and A. M. Zoubir},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Analysis of Activity States of Local Neuronal Microcircuits in Mouse Brain},
  year = {2018},
  pages = {1940-1944},
  abstract = {Time series of neuronal activity corresponding to different activity states in mouse brain are analyzed in the time domain and the time-frequency domain. The signals are associated with either a slow wave brain state or a persistent brain state. For both states, characteristic spectral features are identified and a simple detector is proposed that is able to identify the brain state with low latency and high accuracy. In practice, being able to monitor the brain state online and in real time is crucial for improved in vivo experiments and, ultimately, for a causal understanding of brain dynamics.},
  keywords = {brain;neurophysiology;time series;local neuronal microcircuits;mouse brain;time series;neuronal activity;time domain;time-frequency domain;slow wave brain state;persistent brain state;brain dynamics;activity states;Time-frequency analysis;Feature extraction;Time series analysis;Spectrogram;Transient analysis;Mice;Brain state;neuronal circuits;detection;hypothesis testing;time-frequency analysis},
  doi = {10.23919/EUSIPCO.2018.8553165},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439024.pdf},
}

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M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["D."],"propositions":[],"lastnames":["Jin"],"suffixes":[]},{"firstnames":["B."],"propositions":[],"lastnames":["Boiadjieva"],"suffixes":[]},{"firstnames":["H."],"propositions":[],"lastnames":["Backhaus"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Fauss"],"suffixes":[]},{"firstnames":["T."],"propositions":[],"lastnames":["Fu"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Stroh"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Klein"],"suffixes":[]},{"firstnames":["A.","M."],"propositions":[],"lastnames":["Zoubir"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Analysis of Activity States of Local Neuronal Microcircuits in Mouse Brain","year":"2018","pages":"1940-1944","abstract":"Time series of neuronal activity corresponding to different activity states in mouse brain are analyzed in the time domain and the time-frequency domain. The signals are associated with either a slow wave brain state or a persistent brain state. For both states, characteristic spectral features are identified and a simple detector is proposed that is able to identify the brain state with low latency and high accuracy. In practice, being able to monitor the brain state online and in real time is crucial for improved in vivo experiments and, ultimately, for a causal understanding of brain dynamics.","keywords":"brain;neurophysiology;time series;local neuronal microcircuits;mouse brain;time series;neuronal activity;time domain;time-frequency domain;slow wave brain state;persistent brain state;brain dynamics;activity states;Time-frequency analysis;Feature extraction;Time series analysis;Spectrogram;Transient analysis;Mice;Brain state;neuronal circuits;detection;hypothesis testing;time-frequency analysis","doi":"10.23919/EUSIPCO.2018.8553165","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570439024.pdf","bibtex":"@InProceedings{8553165,\n author = {D. Jin and B. Boiadjieva and H. Backhaus and M. Fauss and T. Fu and A. Stroh and A. Klein and A. M. Zoubir},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Analysis of Activity States of Local Neuronal Microcircuits in Mouse Brain},\n year = {2018},\n pages = {1940-1944},\n abstract = {Time series of neuronal activity corresponding to different activity states in mouse brain are analyzed in the time domain and the time-frequency domain. The signals are associated with either a slow wave brain state or a persistent brain state. For both states, characteristic spectral features are identified and a simple detector is proposed that is able to identify the brain state with low latency and high accuracy. 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