Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/EUSIPCO.2018.8553165 | European Signal Processing Conference |
Keywords | Field | DocType |
Brain state,neuronal circuits,detection,hypothesis testing,time-frequency analysis | Time domain,Time series,Neuroscience,Premovement neuronal activity,Spectrogram,Computer science,Feature extraction,Time–frequency analysis,Latency (engineering),Detector | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Jin | 1 | 19 | 3.07 |
| Boriana Boiadjieva | 2 | 0 | 0.34 |
| Hendrik Backhaus | 3 | 0 | 0.34 |
| Michael Faub | 4 | 0 | 1.35 |
| Ting Fu | 5 | 0 | 0.34 |
| Albrecht Stroh | 6 | 0 | 0.68 |
| Anja Klein | 7 | 173 | 89.48 |
| Abdelhak M. Zoubir | 8 | 1036 | 148.03 |