EEG based topography analysis in string recognition task
| Autor: | Xiaofei Ma, Yuxiaotong Shen, Zike Qin, Xiaolin Huang, Ying Chen, Xinbao Ning, Yun Ge |
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| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Modality (human–computer interaction) medicine.diagnostic_test business.industry 05 social sciences String (computer science) Pattern recognition Electroencephalography Condensed Matter Physics 050105 experimental psychology Task (project management) Sample entropy 03 medical and health sciences 0302 clinical medicine Frequency domain Face (geometry) medicine 0501 psychology and cognitive sciences Artificial intelligence business 030217 neurology & neurosurgery Word (computer architecture) Mathematics |
| Zdroj: | Physica A: Statistical Mechanics and its Applications. 469:531-539 |
| ISSN: | 0378-4371 |
| DOI: | 10.1016/j.physa.2016.11.105 |
| Popis: | Vision perception and recognition is a complex process, during which different parts of brain are involved depending on the specific modality of the vision target, e.g. face, character, or word. In this study, brain activities in string recognition task compared with idle control state are analyzed through topographies based on multiple measurements, i.e. sample entropy, symbolic sample entropy and normalized rhythm power, extracted from simultaneously collected scalp EEG. Our analyses show that, for most subjects, both symbolic sample entropy and normalized gamma power in string recognition task are significantly higher than those in idle state, especially at locations of P4, O2, T6 and C4. It implies that these regions are highly involved in string recognition task. Since symbolic sample entropy measures complexity, from the perspective of new information generation, and normalized rhythm power reveals the power distributions in frequency domain, complementary information about the underlying dynamics can be provided through the two types of indices. |
| Databáze: | OpenAIRE |
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