نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 ﺩﺍﻧﺸﺠﻮی ﺩﻛﺘﺮی، ﮔﺮﻭﻩ ﻣﻬﻨﺪﺳﻰ ﭘﺰﺷﻜﻰ، ﻗﻄﺐ ﻋﻠﻤﻰ ﻛﻨﺘﺮﻝ ﻭ ﭘﺮﺩﺍﺯﺵ ﻫﻮﺷﻤﻨﺪ، ﺩﺍﻧﺸﻜﺪﻩ ﻣﻬﻨﺪﺳﻰ ﺑﺮﻕ ﻭ ﻛﺎﻣﭙﻴﻮﺗﺮ، ﭘﺮﺩﻳﺲ ﺩﺍﻧﺸﻜﺪﻩﻫﺎی ﻓﻨﻰ، ﺩﺍﻧﺸﮕﺎﻩ ﺗﻬﺮﺍﻥ

2 ﺩﺍﻧﺸﻴﺎﺭ، ﮔﺮﻭﻩ ﻣﻬﻨﺪﺳﻰ ﭘﺰﺷﻜﻰ، ﻗﻄﺐ ﻋﻠﻤﻰ ﻛﻨﺘﺮﻝ ﻭ ﭘﺮﺩﺍﺯﺵ ﻫﻮﺷﻤﻨﺪ، ﺩﺍﻧﺸﻜﺪﻩ ﻣﻬﻨﺪﺳﻰ ﺑﺮﻕ ﻭ ﻛﺎﻣﭙﻴﻮﺗﺮ، ﭘﺮﺩﻳﺲ ﺩﺍﻧﺸﻜﺪﻩﻫﺎی ﻓﻨﻰ، ﺩﺍﻧﺸﮕﺎﻩ ﺗﻬﺮﺍﻥ، - ﭘﮋﻭﻫﺸﻜﺪﻩ ﻋﻠﻮﻡ ﺷﻨﺎﺧﺘﻰ، ﭘﮋﻭﻫﺸﮕﺎﻩ ﺩﺍﻧﺶﻫﺎی ﺑﻨﻴﺎﺩی

10.22041/ijbme.2014.13050

چکیده

ﺩﺭ ﺩﺍﺩﻩﻫﺎی EEG/MEG، ﺁﺭﺗﻴﻔﻜﺖ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﺑﻪ ﺻﻮﺭﺕ ﺗﺮﻛﻴﺐ ﺧﻄﻰ ﻟﺤﻈﻪﺍی ﻓﻌﺎﻟﻴﺖ ﻣﻨﺎﺑﻊ ﻣﻐﺰی ﺩﺭ ﻛﺎﻧﺎﻝﻫﺎ ﻣﺸﺎﻫﺪﻩ ﻣﻰﺷﻮﺩ. ﻳﻜﻰ ﺍﺯ ﻭﻳﮋﮔﻰﻫﺎی ﻣﻬﻢ ﺗﺨﻤﻴﻦﮔﺮﻫﺎی ﺍﻳﺪﻩﺁﻝ ﺍﺭﺗﺒﺎﻃﺎﺕ ﻣﻐﺰی، ﻣﻘﺎﻭﻣﺖ ﺑﻪ ﺁﺭﺗﻴﻔﻜﺖ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﺍﺳﺖ؛ ﻳﻌﻨﻰ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﻣﻨﺎﺑﻊ ﻣﻐﺰی ﻣﺴﺘﻘﻞ ﻫﺮﮔﺰ ﻧﺒﺎﻳﺪ ﻣﻨﺠﺮ ﺑﻪ ﺗﺨﻤﻴﻦ ﺍﺭﺗﺒﺎﻃﺎﺕ ﻣﻌﻨﻰﺩﺍﺭی ﺑﻴﻦ ﻛﺎﻧﺎﻝﻫﺎی EEG/MEG ﺷﻮﺩ. ﺗﺎﻛﻨﻮﻥ ﻫﻴﭻ ﻣﻌﻴﺎﺭی ﺑﺮﺍی ﻣﻘﺎﻳﺴﻪ ﺳﻄﺢ ﻣﻘﺎﻭﻣﺖ ﺗﺨﻤﻴﻦﮔﺮﻫﺎی ﻣﺨﺘﻠﻒ ﺍﺭﺗﺒﺎﻃﺎﺕ ﻣﻐﺰی ﺩﺭ ﻣﻘﺎﺑﻞ ﺁﺭﺗﻴﻔﻜﺖ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﺩﺭ ﻛﺎﺭﺑﺮﺩﻫﺎی ﻭﺍﻗﻌﻰ ﺍﺭﺍﺋﻪ ﻧﺸﺪﻩ ﺍﺳﺖ. ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ، ﻣﻌﻴﺎﺭی ﺑﺎ ﻋﻨﻮﺍﻥ ﺷﺎﺧﺺ ﻣﻘﺎﻭﻣﺖ (RI) ﺑﺮﺍی ﺑﺮﺭﺳﻰ ﺳﻄﺢ ﻣﻘﺎﻭﻣﺖ ﺗﺨﻤﻴﻦﮔﺮﻫﺎ ﺑﻪ ﺍﺭﺗﺒﺎﻃﺎﺕ ﺑﻴﻦ ﻛﺎﻧﺎﻟﻰ ﻛﻪ ﺑﺎ ﺗﺮﻛﻴﺐ ﺧﻄﻰ ﻟﺤﻈﻪﺍی ﻣﺆﻟﻔﻪﻫﺎی ﺷﺒﻪ- ﻣﺴﺘﻘﻞ ﻗﺎﺑﻞ ﻣﺪﻝﺳﺎﺯی ﻫﺴﺘﻨﺪ؛ ﺍﺭﺍﺋﻪ ﻣﻰﺷﻮﺩ. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻣﺎﻫﻴﺖ ﺗﺮﻛﻴﺐ ﺧﻄﻰ ﻟﺤﻈﻪﺍی ﺁﺛﺎﺭ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ، ﺍﻧﺘﻈﺎﺭ ﻣﻰﺭﻭﺩ RI ﺑﺘﻮﺍﻧﺪ ﺗﺨﻤﻴﻦﮔﺮﻫﺎی ﺍﺭﺗﺒﺎﻃﺎﺕ ﻣﻐﺰی ﺭﺍ ﺳﺎﺯﮔﺎﺭ ﺑﺎ ﺳﻄﺢ ﻣﻘﺎﻭﻣﺖ ﺁﻧﻬﺎ ﺑﻪ ﺁﺭﺗﻴﻔﻜﺖ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﺭﺗﺒﻪﺑﻨﺪی ﻛﻨﺪ. ﺩﺭ ﺍﺩﺍﻣﻪ، ﺍﺯ RI ﺑﺮﺍی ﺭﺗﺒﻪﺑﻨﺪی ﻫﻔﺖ ﺗﺨﻤﻴﻦﮔﺮ ﺍﺭﺗﺒﺎﻃﺎﺕ ﻛﺎﺭﻛﺮﺩی ﻣﻐﺰی، ﺍﺳﺘﻔﺎﺩﻩ ﻣﻰﺷﻮﺩ؛ ﻛﻪ ﻋﺒﺎﺭﺗﻨﺪ ﺍﺯ: ﺍﻧﺪﺍﺯﻩ ﺿﺮﻳﺐ ﻫﻤﺒﺴﺘﮕﻰ ﭘﻴﺮﺳﻮﻥ (CC)، ﺍﻃﻼﻋﺎﺕ ﻣﺘﻘﺎﺑﻞ (MI)، ﻣﺠﺬﻭﺭ ﺍﻧﺪﺍﺯﻩ ﻛﻮﻫﺮﻧﺲ (Coh)، ﻣﻘﺪﺍﺭ ﻗﻔﻞﺷﺪﮔﻰ ﻓﺎﺯ (1:1) ((1:1)PLV)، ﺍﻧﺪﺍﺯﻩ ﺟﺰﺀ ﻣﻮﻫﻮﻣﻰ ﻛﻮﻫﺮﻧﺴﻰ (ImC)، ﺷﺎﺧﺺ ﺗﺄﺧﻴﺮ ﻓﺎﺯ (PLI) ﻭ ﺷﺎﺧﺺ ﺗﺄﺧﻴﺮ ﻓﺎﺯ ﻭﺯﻥﺩﺍﺭ (WPLI). ﻧﺘﺎﻳﺞ ﺑﺮﺍی ﺩﺍﺩﻩﻫﺎی ﺷﺒﻴﻪﺳﺎﺯی ﺷﺪﻩ ﻭ ﺳﻴﮕﻨﺎﻝﻫﺎی ﻭﺍﻗﻌﻰ EEG ﻧﺸﺎﻥ ﻣﻰﺩﻫﻨﺪ ﻛﻪ ﺗﺨﻤﻴﻦﮔﺮﻫﺎﻳﻰ ﻛﻪ ﺍﺯ ﻟﺤﺎﻅ ﺗﺌﻮﺭی ﺑﻪ ﺁﺭﺗﻴﻔﻜﺖ ﺑﺎﻻﺗﺮﻳﻦ ﺭﺗﺒﻪﻫﺎ ﺭﺍ ﺩﺍﺭﻧﺪ. ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﻣﻘﺎﻭﻡ ﻫﺴﺘﻨﺪ (ImC، PLI و WPLI) ﻣﻘﺎﺩﻳﺮ RI ﻧﺰﺩﻳﮏ %100 ﻣﻰﺩﻫﻨﺪ ﻭ ﻣﻄﺎﺑﻖ ﺍﻧﺘﻈﺎﺭ ﻫﻤﭽﻨﻴﻦ، ﺑﺮﺍی ﺩﺍﺩﻩﻫﺎی ﺷﺒﻴﻪﺳﺎﺯی ﻛﻪ ﺁﺛﺎﺭ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﻭ ﻣﻨﺎﺑﻊ ﻣﻐﺰی ﻣﺸﺨﺺ ﺍﺳﺖ، ﺭﺗﺒﻪﺑﻨﺪی ﺗﺨﻤﻴﻦﮔﺮﻫﺎ ﺑﺎ RI ﺳﺎﺯﮔﺎﺭ ﺑﺎ ﺳﻄﺢ ﻣﻘﺎﻭﻣﺖ ﺁﻧﻬﺎ ﻧﺴﺒﺖ ﺑﻪ ﺁﺭﺗﻴﻔﻜﺖ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﺍﺳﺖ. ﺍﻳﻦ ﺍﻣﺮ ﺍﻣﻜﺎﻥ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ RI ﺭﺍ ﺑﺮﺍی ﺭﺗﺒﻪﺑﻨﺪی ﺳﻄﺢ ﻣﻘﺎﻭﻣﺖ ﺗﺨﻤﻴﻦﮔﺮﻫﺎ ﺑﻪ ﺁﺭﺗﻴﻔﻜﺖ ﻫﺪﺍﻳﺖ ﺣﺠﻤﻰ ﺑﺮﺍی ﺩﺍﺩﻩﻫﺎی ﻭﺍﻗﻌﻰ EEG/MEG ﺗﺄﻳﻴﺪ ﻣﻰﻛﻨﺪ. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Novel Criterion for Ranking the Robustness of EEG/MEG Sensor-Space Connectivity Estimators against Volume Conduction Artifact

نویسندگان [English]

  • Ali Khadem 1
  • Gholam Ali Hossein-Zadeh 2

1 PHD Candidate of Biomedical Engineering, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran

2 Associate Professor of Biomedical Engineering, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran - School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM)

چکیده [English]

In EEG/MEG datasets, the Volume Conduction (VC) artifact appears as instantaneous linear mixing of brain source activities on the channel measurements. A desired characteristic of an ideal EEG/MEG connectivity estimator (on sensor-space) is its robustness to VC artifact. This means that the VC of independent brain sources must never lead to detection of significant connectivity among EEG/MEG channels. There has been no criterion in the literature so far that can compare the robustness levels of different (sensor-space) connectivity estimators against VC artifact. In this paper, a criterion called Robustness Index (RI) is proposed to compare the robustness levels of connectivity estimators to channel couplings which are modeled by instantaneous linear mixing of quasi-independent components. Since the VC effects have instantaneous linear mixing nature, we expect RI to rank the connectivity estimators according to their robustness levels to VC artifact. RI is used to rank seven functional connectivity estimators: the absolute value of Pearson Correlation Coefficient (CC), Mutual Information (MI), Magnitude Squared Coherence (Coh), (1:1) Phase Locking Value ((1:1)PLV), the absolute value of Imaginary part of Coherency (ImC), Phase Lag Index (PLI) and Weighted Phase Lag Index (WPLI). The results for simulated data and a real EEG dataset show the connectivity estimators that are theoretically robust to VC artifact (ImC, PLI and WPLI) yield RI values near %100 and have the highest ranks, as expected. Also, for the simulated models in which the true VC effects and brain sources are known, ranking the connectivity estimators by RI is consistent with their robustness levels against VC artifact. This supports the possibility of using RI as a tool for ranking the robustness levels of connectivity estimators against VC artifact for real EEG/MEG datasets.

کلیدواژه‌ها [English]

  • Volume Conduction Artifact
  • EEG/MEG
  • Independent Component Analysis (ICA)
  • Surrogate Data
  • Connectivity Estimators
  • Robustness Index (RI)

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