情報処理学会研究報告 つの電極を搭載している これにより電極と頭皮の間にゲルを使用することなく 電気抵抗 の少ないクリアな信号を得ることができる Enobio では EEG Electro-encephalogram 脳波 EOG Electro-oculogram 眼球運動 ECG Electro
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1 Enobio α Enobio α EEG Enobio ICA FFT α The estimation of alpha wave shapes with Enobio Yu Ishikawa, 1 Masami Takata, 2 Tomohiro Umeda 3 and Kazuki Joe 2 In this paper, we try to estimate alpha wave shapes with an EEG electrophysiology recording system, Enobio. We have implemented a real time Enobio analysis environment to interactively analyze EEGs. To apply additional various signal prossesing algorithms, we improve the analysis environment and implement several signal prossesing functions. ICA, FFT, FFT based bandpass filter, median filter and cepstrum analysis are to be applied for original data so that we try to estimate alpha wave shapes. 1 Department of Information and Computer Sciences, Faculty of Science, Nara Women s University 2 Graduate School of Humanities and Sciences, Nara Women s University 3 Social Cooperation Center, Nara Women s University 1. BMI(Brain-Machine Interface ) 1) BMI BMI 1 BMI 4 Enobio 2) Enobio BMI α α 3) α 1929 Hans Berger Berger 8-12Hz α 1934 Adrian α α α 1972 α α α 1936 Davis α α (percent time alpha) 4 α 1 waxing and waning α α Enobio α 1 c 2011 Information Processing Society of Japan
2 情報処理学会研究報告 つの電極を搭載している これにより電極と頭皮の間にゲルを使用することなく 電気抵抗 の少ないクリアな信号を得ることができる Enobio では EEG Electro-encephalogram 脳波 EOG Electro-oculogram 眼球運動 ECG Electro-cardiogram 心電図 の 3 種類の信号 3) を計測することができる ECG の計測時には 電極の 1 つを手首に固定 して計測する このとき ECG と同時に残りの 3 つの電極で EEG を計測することが可能 である 各信号はワイヤレスで TCP/IP により外部に出力することが可能である a. 通常 図1 b. キャップ有り Enobio で測定可能な電極は 世界標準とされている国際 法5) の 13 F7 1 Enobio 装着時の様子 Fp1 2 Fp2 14 F8 に相当する 国際 法による電極配置を図 2 に示 す 基準電極として 付属のアース電極を片方の耳たぶに装着する その他 オプションと して電極を装着するためのネットがある ネットを用いた Enobio 装着時の様子を図 1 - b に示す これにより任意の場所に電極を配置することができ 前頭部のみの計測に限らず 前頭部と後頭部に 2 つずつ電極を配置することも可能である Enobio の付属アプリケーションとして Enobio Server User Interface がある これによ り Enobio から計測された脳波をリアルタイムで観察することができる しかし 付属アプ リケーションでは Enobio から得られた実測値を観察することしかできず ノイズ除去や周 図 2 国際 法 波数解析などの処理を行い 加工されたデータを観察する機能が不足している これまでに BMI 開発に必要となるデータの観察や解析を容易に行うことを補助する目 波に様々な波形処理を適用する 的で リアルタイム Enobio 解析環境の開発について文献 4) で述べている 2 章で実験環境について説明し 3 章で実験と結果について述べ 4 章で 3 章の実験結果 この解析環境は TCP/IP を利用して Enobio の測定値を取得するデータ取得部 測定 についての考察を行う 値に ICA Independent component analysis, 独立成分分析 と DFT discrete fourie transform 離散フーリエ変換 の信号処理を行うデータ処理部 処理されたデータを表示 2. 実 験 環 境 するデータ表示部 以上 3 つの機能を制御する GUI 部の 4 部構成となる 図 3 - a が既存 本章では Enobio を用いた脳波解析の実験環境について述べる 2.1 節では 本実験で の GUI である 2.2 リアルタイム Enobio 解析環境の改良 使用する脳波計測システムの Enobio とデータ解析アプリケーションであるリアルタイム Enobio 解析環境 4) について説明し 2.2 節では リアルタイム Enobio 解析環境の改良点 既存のリアルタイム解析環境を用いることで Enobio から得られたデータをリアルタイ について述べる ムで処理し観察することが可能となる しかし 問題点も多く残されており 脳波解析を 2.1 Enobio とリアルタイム Enobio 解析環境 行う上で解析環境の改良が必要となる 各処理の高速化と精度の向上のため 4 GB のメモ Enobio はスペインの Starlab 社が開発した非侵襲型の電気生理学情報記録システムであ リ Intel(R) Core(TM) i5-2500k 3.30GHz の CPU 搭載の PC を使用する OS る Enobio は主に 4 つの電極とケーブル 発信機の ENOBIO Sensor Communication は Windows XP から Ubuntu へ変更して開発を行う Module 受信機の ENOBIO USB receiver 電極と発信機を装着するためのヘッドバンドで 以下で 各モジュールの改良点について述べる 構成されている Enobio 装着時の様子を図 1 - a に示す Enobio は乾燥状態で計測できる 4 データ取得部では 既存の解析環境の OS が Windows であるため winsock を用いて 2 c 2011 Information Processing Society of Japan
3 a. b. 3 Enobio TCP/IP Unix OS TCP/IP ICA DFT ICA FFT fast fourie transform BMI Enobio 1 BMI ICA ICA FastICA 6) FastICA C++ IT++ 7) ICA IT++ 3 CuBICA 8) CuBICA 1 ECG 3 ICA Enobio DFT DFT DFT FFT δ θ α β 3) α 8-13Hz α α FFT API OpenGL 1 GUI GUI Visual C++ GUI GTK+ 9) 3 - b GUI TCP/IP Enobio 3 c 2011 Information Processing Society of Japan
4 a. ECG EEG b. ECG 4 ECG a. b. 10) IFFT FFT 8-13Hz α Enobio 250/s Hz 8-13Hz F7, Fp1, Fp2, F8 FFT 2 1 ECG EEG 32 2 EEG α 1 ECG EEG 1 ECG a 1 ECG b ICA ICA 8-13Hz α α 8-13 Hz α FFT Hz 0-1Hz 1-40Hz 40Hz ICA 4 4 ICA 7 - a b ICA 8-13Hz 4 c 2011 Information Processing Society of Japan
5 a. ICA b. ICA a. b. 8-13Hz 7 ICA 9 a. b. a. ICA b. ICA 8-13Hz Hz 8 ICA ICA FFT ICA 8 - a Hz ICA 8-13Hz 8 - b a 9 - a 8-13Hz 9 - b Hz 8-13Hz α 10 - a 10 - b ECG a 4 - b R EEG ECG ECG EEG Hz 11) α 2 2 α c 2011 Information Processing Society of Japan
6 α ICA 7 - a b α 7 - b Hz ICA ICA α 2 ICA 8-13Hz α 7 - b 8 - b 7 - b 8 - b 9 - a 9 - b α 8-13Hz α Hz α 10 - a b 10 - a 10 α α 5. Enobio FFT ICA ECG ICA ICA α α α α α α LPC Linear Predictive Coding α STFT short-time fourier transform α α δ 1) Mikhail A. Lebedev and Miguel A.L. Nicolelis.: Brain machine interfaces:past, present and future, TRENDS in Neurosciences Vol.29 pp (2006). 2) Starlab: Starlab Living Science, available from (accessed 2011). 3) : 5 (1999). 4) Y Ishikawa, S Teramae, N Yoshii, M Takata and K Joe:A Real-time Analysis Environment for a Wireless BMI Device Enobio, PDPTA pp (2011). 5) H.H. Jasper: The ten-twenty electrode system of the international federation, Electroencephogr Clin Neurophysiol 10 pp (1958). 6) Hyvrinen, A: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis, IEEE Transactions on Neural Networks, pp (1999). 7) SourceForge: SourceForge.net: Project itpp, available from (accessed ). 8) Blaschke, T. and Wiskott, L.: CuBICA: Independent Component Analysis by Simul- taneous Third- and Fourth-Order Cumulant Diagonalization (2003). 9) The GTK+ Team: The GTK+ Project, available from (accessed ) 10) D. G. Childers, D. P. Skinner, R. C. Kemerait: The Cepstrum: A Guide to Processing, Proceedings of the IEEE, Vol65, No.10 pp (1977). 11) Plamen Manoilov: EEG Eye-Blinking Artefacts Power Spectrum Analysis, Comp- SysTech 06 (2006) 6 c 2011 Information Processing Society of Japan
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