脳源推定のための脳波法:基礎と最前線
Anderson Roy Phillips1, Yash Shashank Vakilna1, Dorsa E P Moghaddam2
1Department of Electrical and Computer Engineering and Neuroengineering Initiative, Rice University, 6100 Main St, Houston, Texas, 77005-1892, UNITED STATES.
Journal of neural engineering
|January 27, 2026
まとめ
脳波法(EEG)は手頃な価格で脳活動を測定できるが、深部源の局在化には課題がある。本レビューは、神経生理学的洞察を深めるための空間分解能向上に向けた高度な逆モデリングと高密度EEGを探求する。
背景:
- 脳波法(EEG)は、脳活動を測定するための費用対効果が高くポータブルなツールである。
- 現在のEEG法は空間分解能に限界があり、深部源の局在化を妨げている。
- 課題には、高次元性、ニューラル信号の重複、解剖学的変動が含まれる。
結論:
- EEGにおけるソースローカライゼーションの向上は、高度な逆モデリングと技術的強化を通じて達成可能である。
- マルチモーダルデータの統合と高密度EEGの利用は、重要な戦略である。
- 本レビューは、神経生理学的源のより良い理解のために、これらの高度な方法を適用する上で研究者を導くことを目的とする。
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