Discrimination of motor imagery tasks via information flow pattern of brain connectivity
| العنوان: | Discrimination of motor imagery tasks via information flow pattern of brain connectivity |
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| المؤلفون: | Pheng-Ann Heng, Qiong Wang, Jing Qin, Wai-Man Pang, Kup-Sze Choi, Shuang Liang |
| المصدر: | Technology and Health Care. 24:S795-S801 |
| بيانات النشر: | IOS Press, 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Computer science, Speech recognition, Biomedical Engineering, Biophysics, Health Informatics, Bioengineering, Electroencephalography, 01 natural sciences, Lateralization of brain function, Biomaterials, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, medicine, Humans, 0101 mathematics, Brain–computer interface, medicine.diagnostic_test, Motor Cortex, Information processing, Information flow, Coherence (statistics), Frequency domain, Nerve Net, Algorithms, Psychomotor Performance, 030217 neurology & neurosurgery, Information Systems |
| الوصف: | BACKGROUND The effective connectivity refers explicitly to the influence that one neural system exerts over another in frequency domain. To investigate the propagation of neuronal activity in certain frequency can help us reveal the mechanisms of information processing by brain. OBJECTIVE This study investigates the detection of effective connectivity and analyzes the complex brain network connection mode associated with motor imagery (MI) tasks. METHODS The effective connectivity among the primary motor area is firstly explored using partial directed coherence (PDC) combined with multivariate empirical mode decomposition (MEMD) based on electroencephalography (EEG) data. Then a new approach is proposed to analyze the connection mode of the complex brain network via the information flow pattern. RESULTS Our results demonstrate that significant effective connectivity exists in the bilateral hemisphere during the tasks, regardless of the left-/right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of the contralateral lateralization. The classification performance of left-/right-hand MI tasks can be improved by careful selection of intrinsic mode functions (IMFs). CONCLUSION The proposed method can provide efficient features for the detection of MI tasks and has great potential to be applied in brain computer interface (BCI). |
| تدمد: | 1878-7401 0928-7329 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd498e159b4c468869d4c0c71bd6de72Test https://doi.org/10.3233/thc-161212Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....dd498e159b4c468869d4c0c71bd6de72 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 18787401 09287329 |
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