JP3799889B2 - Blink motion analysis apparatus and method - Google Patents

Description

となる。ここで、Ψは、マザーウェーブレット(Mother Wavelet)関数と呼ばれ、ハール(Haar)、ドーベシィ(Daubechies)、メキシカンハット(MexicanHat)等様々なものが知られている。この例では、周波数分解能が高いといわれるスプライン４(Spline4)関数をマザーウェーブレット関数に用いてウェーブレット変換を行う。式（１）において、ａはマザーウェーブレット関数の時間軸方向についての伸縮に関するパラメータであり、周波数成分の情報に対応する。またｂはマザーウェーブレット関数を時間軸方向の平行移動に関するパラメータである。この変換を、離散的にサンプリングされたデータに適用できるよう、パラメータａ，ｂを、
【数２】

と定義する。すると式（１）は、
【数３】

と書け、この逆変換は、
【数４】

と表せる。次に式（４）は、
【数５】

を使って、脳波時系列データf(t)を原データf₀(t)と見ることで
【数６】

と書ける。ここで、
【数７】

としたとき、このｊは分解レベルを表していて、ｊの値が小さいほどサンプリング間隔が大きくなることに対応し、低周波成分を検出できるようになる。式（７）から明らかなように、f_j(t)は次のように分解できる。
【００１４】
【数８】

これがウエーブレット分解であり、この式（８）の右辺第２項がウェーブレット展開係数、すなわち高周波成分を表し、右辺第１項が元の信号（左辺）から高周波成分を取り除いて得た低周波成分を表す。この分解操作により得られた低周波成分f_j-1に再び同じ式（８）を適用し、これを繰り返すことにより分解を進めていく。
【００１５】
発明者らは、瞬目動作による波形成分が入っていることを確認している脳波時系列データを多数用意し、これらに対してマザーウェーブレットにスプライン４関数を用いて上記のウェーブレット分解操作を施し、各分解レベルでの低周波成分f_jと高周波成分g_jの信号を調べた。その結果、レベル（−４）まで分解することにより、ほとんどのケースで瞬目動作による波形成分を抽出できることを発見した。
【００１６】
すなわち、
【数９】

と分解することで、f_-4に瞬目動作による脳波成分をとらえることができることが実験により確認できた。
【００１７】
図１に、実験に用いたオリジナルの脳波信号f₀の波形を示す。この波形は、符号１に示す範囲に、脳内活動による通常の脳波成分の他に、瞬目動作による脳波成分が重畳されている。図２は、図１に示した信号f₀を分解して得た、レベル（−１）の低周波成分f_-1、高周波成分g_-1の波形を示し、図３はf_-1を分解して得たレベル（−２）の低周波成分f_-2、高周波成分g_-2の波形を示す。同様に図４はレベル（−３）の、図５はレベル（−４）の、各成分（すなわちf_-3、g_-3、f_-4、g_-4）の波形を示す。この図５の（ａ）に示すレベル（−４）の低周波成分f_-4の波形は、図１のオリジナルの信号の符号１の範囲に該当する部分が十分滑らかになっており、これが瞬目動作による脳波成分の波形を表している。
【００１８】
以上説明したように、予備実験により、前頭極の任意の位置に装着した電極から計測した脳波時系列データに対して、マザーウェーブレットにスプライン４関数を用い、レベル（−４）まで分解することで、瞬目動作により生じた脳波成分を抽出できることが分かった。前頭極の電極の信号には、瞬目成分が現れやすい。
【００１９】
図６は、図５の（ａ）に示したf_-4の波形のうち、瞬目動作による波形部分を模式的に示した図である。図６に示すように、一般的に、瞬目時には、f_-4成分は、一般的に鋭く正の方へ立ちあがり、次に負の方向へ落ち込み、また正の方向へ立ちあがってから徐々に落ち着く軌跡を描く。実験では、用いた様々な脳波について、負の方向への落ち込みは瞬目一回につき一度しかないことを確認した。したがって、図６にて符号２で示す負の方向へ落ち込む部分を検出することで、瞬目情報を得ることが考えられる。実験では、瞬目動作が短期間に連続すると、瞬目波形の形が図６のような典型的な波形から崩れることがあるが、負の方向へは瞬目一回につき一度しかないという原則は崩れないことも確認した。
【００２０】
また、瞬目による脳波成分の振幅は、（瞬目以外の）通常の脳内活動による脳波の振幅より大きいことも実験で確認した。
【００２１】
すなわち、図７に示すように、f_-4成分における１回の瞬目に対応する部分５は、必ず１カ所の下り勾配部４を持ち、かつf_-4成分が負に振れた箇所では、そのピークの絶対値は、脳波信号の負値の平均レベル３の絶対値よりも大きくなっている。
【００２２】
これらから、本実施形態では、レベル（−４）のウェーブレット分解結果の低周波成分f_-4の傾きが負で、かつf_-4の絶対値が脳波振幅の平均より所定割合α以上大きくなった時点をもって、瞬目動作が起こったと判定するようにした。所定割合以上としたのは、誤判定を防ぐための余裕を考慮したためである。
【００２３】
このため、まず前頭極に装着した電極で検出した脳波の時系列データＸ（ｉ）（ｉは時刻）のうち、Ｘ（ｉ）＜０を満たす値Ｘの平均を求める。
【００２４】
【数１０】

ここでＮは、この平均値を求めるために取り出したデータ数である。
【００２５】
そして、次の式（１１）及び（１２）の２つの条件式を同時に満足したときに瞬目動作をしたと判定することとした。
【００２６】
【数１１】

【数１２】

以上を瞬目動作アルゴリズムと呼ぶことにする。これは、f_-4の傾きが負で、かつその値が閉眼時の脳波の振幅平均のα倍になったときに、瞬目動作の波形をとらえたと判定することを意味する。
【００２７】
式（１２）の右辺の微分は、レベル（−４）におけるサンプリング間隔をΔｔとして、
【数１３】

で求める。
【００２８】
ここで上記係数αは、ユーザが表示波形の状態を見て選択できるようにした。実験によれば、αの選択範囲は、１．４≦α≦２．０に限定することが望ましいことが分かった。このように、αの選択範囲に幅を持たせたのは、閉眼時の脳波波形の振幅には個人差があり、さらに瞬目波形の振幅にも個人差があるので、αを固定値にしてしまうと、ある人には適用できても違う人には適用できなくなる可能性があるからである。
【００２９】
また、αの選択範囲の下限１．４及び上限２．０は、実験により定めた。すなわち、多数のケースについての分析から、αを１．４より小さい値にすると、瞬目動作でない通常の脳内活動による脳波波形を、瞬目動作と誤って判定してしまう可能性があることがわかった。αを２．０より大きい値にすると、瞬目波形が小さい場合に検出漏れを起こす可能性があることも分かった。以上のことから、（１１）及び（１２）の両式を満たしたときに瞬目動作が起こったと判定し、それら両式を満足した時刻を、瞬目発生時刻とすることとした。
【００３０】
図８に、本実施形態の装置の概略構成を示す。この装置は、脳波計１０、脳波解析表示処理部２０及び表示装置３０を含む。脳波計１０は従来からある一般的な脳波計である。脳波解析表示処理部２０は、脳波計１０で得られた脳波信号（時系列データ）に対し瞬目動作に関する解析を行い、その解析結果と脳波波形とを示す表示画像情報を生成する。表示装置３０は、脳波解析表示処理部２０で生成された表示画像情報を表示するＣＲＴ、液晶ディスプレイなどの表示装置である。
【００３１】
図９は、この装置の脳波解析表示処理部２０の瞬目動作解析の手順を示すフローチャートである。以下、図８及び図９を参照して、本実施形態の装置の構成及び動作を説明する。
【００３２】
脳波計１０の電極は、例えば、前頭極にFp1、Fp2、前頭にF3、Fz、F4、下前頭にF7、F8、中心頭にC3、Cz、C4、頭頂にP3、Pz、P4、後頭にO1、O2、側頭にT3、T4後側頭にT5、T6、耳朶にA1、A2を、それぞれ装着する。この方法は国際脳波学会で標準方式として推奨している１０／２０法である。ただし、この電極装着法はあくまで一例であり、本実施形態の手法は脳波電極装着法には基本的に依存しない。例えば将来新たな方式が出現してきたらその方式で電極を装着しても良い。
【００３３】
この脳波計１０で検出された生の脳波信号は、増幅及びＡ／Ｄ変換されて時系列データとなり、脳波解析表示処理部２０の表示画像生成部２６とウェーブレット分解部２２に入力される（図９のＳ１００）。
【００３４】
ウェーブレット分解部２２は、入力された脳波時系列データに対してマザーウェーブレットとしてスプライン４関数を用いてウェーブレット変換を施し（Ｓ１０２）、レベル（−４）までウェーブレット分解して、瞬目による脳波成分を通常脳内活動の脳波成分から分離する（Ｓ１０４）。この分解結果は瞬目解析部２４に送られる。瞬目解析部２４では、受け取ったウェーブレット分解結果の信号群からレベル（−４）の低周波成分f_-4を抽出し（Ｓ１０６）、この抽出結果に対して式（１１）及び（１２）に示した瞬目判定条件を満足するかどうかの判定を行う（Ｓ１０８）。この判定結果がＹｅｓの場合は瞬目動作があったと判断し、Ｎｏの場合は瞬目動作がないと判断する。
【００３５】
この手順では、瞬目判定条件が満たされた時刻を瞬目動作の発生時刻とする。また、瞬目解析部２４にて、一連の脳波計測結果の時系列データに対してこの判定を連続して行い、瞬目判定基準を満足した回数を計数することで、計測中に発生した瞬目動作の累計発生回数を求めることができる。さらに、所定の単位時間間隔当たりの瞬目動作の回数を計数するようにすることもできる。この単位時間間隔を、ユーザから設定できるようにすると、ユーザの目的に応じた解析結果が求められる。
【００３６】
このようにして瞬目解析部２４で求められた瞬目発生時刻や累計発生回数、単位時間当たりの発生回数の情報は、表示画像生成部２６に渡される。表示画像生成部２６は、受け取った瞬目発生時刻や回数の情報を、脳波計１０から受け取った脳波信号データの波形表示画像と合成し、表示装置３０に供給する。瞬目解析結果と脳波信号の表示結果との合成は、瞬目解析部２４での処理遅延に応じて時相を合わせるように行うことが好適である。
【００３７】
このような構成により、ユーザは、表示装置３０の表示により、時々刻々変化する脳波波形に併せて、瞬目動作の発生や、その回数などに関する情報を得ることができる。しかも、この装置では、被験者に対して脳波計１０の電極を装着するだけで瞬目動作に関する情報が得られる。したがって、瞬目動作検出のための専用の検出装置を被験者に装着する必要がないので、被験者の負担が軽減される。
【００３８】
なお、以上の例では、脳波計の電極は頭部全体に装着したが、瞬目動作検出だけならば、前頭極の電極と基準電位の電極だけでよい。
【００３９】
以上示した本実施形態の瞬目動作解析は、脳波計１０により時々刻々検出される脳波信号に対してリアルタイムで適用することもできるし、過去に脳波計１０で計測し記憶装置に記憶した脳波時系列データを取り出して、それに対して適用することもできる。なお、リアルタイム処理の場合、瞬目解析処理に長い時間を要すると、表示装置３０に表示される波形のリアルタイム性が損なわれてしまうおそれがあるが、パーソナルコンピュータでも高速なものを用いれば、実用上問題ない程度の時間で解析が行える。
【００４０】
脳波解析表示処理部２０は、例えばパーソナルコンピュータなどのコンピュータシステムをプラットフォームとして、ソフトウエア的に実装することができる。この場合、以上に説明した処理手順を記述したプログラムをコンピュータシステムに実行させればよい。このプログラムは、ＣＤ−ＲＯＭ等の可搬記録媒体の形でベンダから提供することができる。ユーザはこの記録媒体上のプログラムを自分のコンピュータシステムにインストールすることにより、脳波解析表示処理部２０を構成することができる。
【００４１】
以上、本発明の好適な実施形態について説明したが、これはあくまで一例に過ぎず、様々な変形例が本発明の範囲内に含まれる。
【００４２】
例えば、上記の例では、ウェーブレット分解のマザーウェーブレットにスプライン４関数を用いたが、他のマザーウェーブレット関数を用いることも可能である。スプライン４関数を用いた場合はレベル（−４）までの分解で瞬目動作による脳波成分を抽出できたが、他の関数を用いた場合は、それに応じて分解レベルのレベル数を決める必要がある。実験等によりどの分解レベルまで分解すればよいかを求め、これに応じてウェーブレット分解部２２のウェーブレット分解のレベル数の設定を変えればよい。
【００４３】
また、例示した瞬目判定条件は一例であり、これ以外の判定条件を用いることもできる。
【００４４】
【発明の効果】
以上説明したように、本発明によれば、脳波測定結果から瞬目動作に関する情報を抽出することができるので、脳波検出装置を被験者に装着するだけで、脳波のみならず瞬目動作の情報を得ることができ、被験者の負担を軽減できる。
【図面の簡単な説明】
【図１】 瞬目による成分を含んだオリジナルの脳波データの波形を示す図である。
【図２】 オリジナルデータをウェーブレット分解して得たレベル（−１）の低周波成分f-1、高周波成分g-1の波形を示す図である。
【図３】 レベル（−２）の分解結果の波形を示す図である。
【図４】 レベル（−３）の分解結果の波形を示す図である。
【図５】 レベル（−４）の分解結果の波形を示す図である。
【図６】 瞬目波形の特徴を説明するための図である。
【図７】 瞬目波形の判定条件を説明するための図である。
【図８】 実施形態の装置構成を示す図である。
【図９】 瞬目解析処理の手順を示すフローチャートである。
【符号の説明】
１０ 脳波計、２０ 脳波解析表示処理部、２２ ウェーブレット分解部、２４ 瞬目解析部、２６ 表示画像生成部、３０ 表示装置。[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an apparatus and method for detecting and analyzing blink movement from an electroencephalogram signal.
[0002]
[Prior art]
As a technique for measuring a human concentration level, for example, there is a “concentration level estimation device” described in Japanese Patent Laid-Open No. 9-262216. In this apparatus, measurement is performed from the nerve activity of a subject, a plurality of pieces of biological information are measured, and the measurement information and concentration setting rule information are estimated. In addition, in the “feature brain electromagnetic wave detection device” described in Japanese Patent Application Laid-Open No. 11-137530, a wave brain analysis is performed on a brain electromagnetic wave in which feature waves caused by pulse waves, eye movements, blinks, etc. are mixed. The feature wave component is determined by comparing electromagnetic wave data and biological information time-series data. This device is intended to reduce the burden on those who read brain waves, such as EEG readers and doctors. This device is equipped with detection means for detecting pulse waves, eye movements, blinks, etc. The characteristic wave is determined based on the detection signal of the means. Furthermore, in the “worker's psychosomatic state evaluation method, work content control method and work content control system for work using equipment” described in Japanese Patent Laid-Open No. 11-65422, detection signals of skin impedance sensors and blink sensors are used. The mental and physical state of the worker is determined. The blink sensor measures the blink frequency.
[0003]
[Problems to be solved by the invention]
When working toward a display such as a personal computer, it is said that the number of blinks of the worker decreases as the degree of concentration or interest increases. In this way, information on blinking movement is an important element in knowing human mental activity.
[0004]
By the way, electroencephalogram measurement is widely performed as a method for observing human brain activity. When eye movement information is detected simultaneously with this electroencephalogram measurement and a complex analysis is to be performed using electroencephalogram and other biological information, it is necessary to attach a sensor such as a blink sensor to the subject in addition to the electroencephalograph. The burden on the subject was great. Moreover, there is a possibility that the electroencephalograph and the blink sensor may interfere with each other, which may reduce the reliability of the measurement data.
[0005]
In addition, the technique described in the above-mentioned Japanese Patent Application Laid-Open No. 11-137530 detects biological information such as pulse waves and blinks with another sensor simultaneously with the brain wave, but biological information other than the brain wave detected at this time Is used only to identify feature waves in the electroencephalogram, and this biological information is not analyzed to analyze biological phenomena such as blinks.
[0006]
The present invention has been made in view of such circumstances, and an object of the present invention is to provide an apparatus and a method for obtaining information on blinks simultaneously with an electroencephalogram without increasing the burden on the subject.
[0007]
[Means for Solving the Problems]
In order to solve the above problem, in the present invention, the time series data of brain waves is decomposed by wavelet analysis and separated into blink waveform data and normal brain wave data. By using the separated blink waveform data, blink confirmation information is obtained.
[0008]
The blink movement analysis device according to the present invention includes an electroencephalogram detection means, a decomposition means for wavelet decomposition of time series data of the electroencephalogram detected by the electroencephalogram detection means to a predetermined decomposition level, and the predetermined decomposition obtained by the decomposition means. Blinking analyzing means for obtaining information related to blinking operation from a low frequency component in the level decomposition result.
[0009]
For example, in a preferred aspect, the slope of the low frequency component in the decomposition result of the predetermined decomposition level is negative, and the value of the low frequency component is α times the average of the negative values of the electroencephalogram time series data (α is a constant). The presence / absence of a blink operation is determined using a small value as a blink determination condition.
[0010]
For example, the wavelet decomposition uses a spline 4 function as a mother wavelet, and decomposes to a level (−4) as the predetermined decomposition level.
[0011]
DETAILED DESCRIPTION OF THE INVENTION
As a preliminary experiment for the development of the apparatus of the present embodiment, the inventor detects an eye movement by attaching an electrode of a blink sensor around the eye of the subject, and in parallel with this, detects an electroencephalogram by an electroencephalograph. Measurement was performed. Then, by comparing the detection result of the electroencephalograph when the blink operation was performed with the detection result of the blink sensor, it was examined how the electroencephalogram time-series data changes due to the blink operation. At this time, the brain wave time series data which changes with various types of blink operations was recorded by having the subject perform blink operations at various time intervals. From this preliminary experiment, we captured the characteristics of the blink-derived components mixed in the electroencephalogram waveform.
[0012]
In this experiment, wavelet transform was applied to EEG time-series data, which was found to contain waveforms from blinks. The wavelet transform itself is a well-known technique. For example, the mathematical science wavelet beginers guide, Sugawara Susumu Tokyo Denki University Press, 1995, and the like are well known. Hereinafter, the wavelet transform process used in this embodiment will be briefly described.
[0013]
If the EEG time-series data measured at time t is f (t), the expansion coefficient of the wavelet transform for the waveform f (t) is

It becomes. Here, Ψ is called a mother wavelet function, and various types such as Haar, Daubechies, and Mexican Hat are known. In this example, wavelet transformation is performed using a spline 4 (Spline4) function, which is said to have a high frequency resolution, as a mother wavelet function. In Expression (1), a is a parameter related to expansion / contraction in the time axis direction of the mother wavelet function, and corresponds to information on frequency components. Further, b is a parameter relating to the parallel movement of the mother wavelet function in the time axis direction. In order to apply this transformation to discretely sampled data, the parameters a and b are
[Expression 2]

It is defined as Then equation (1) becomes
[Equation 3]

This inverse transform is
[Expression 4]

It can be expressed. Next, Equation (4) is
[Equation 5]

Using EEG, the EEG time series data f (t) is regarded as the original data f ₀ (t).

Can be written. here,
[Expression 7]

Where j represents the decomposition level, and the smaller the value of j, the larger the sampling interval and the lower frequency components can be detected. As is clear from Equation (7), f _j (t) can be decomposed as follows.
[0014]
[Equation 8]

This is wavelet decomposition, the second term on the right side of the equation (8) represents the wavelet expansion coefficient, that is, the high frequency component, and the first term on the right side is the low frequency component obtained by removing the high frequency component from the original signal (left side). Represents. The same expression (8) is applied again to the low frequency component f _j−1 obtained by this decomposition operation, and the decomposition is advanced by repeating this.
[0015]
The inventors prepared a large number of electroencephalogram time-series data that have been confirmed to contain waveform components due to blinking, and applied the above wavelet decomposition operation to the mother wavelet using a spline 4 function. The signals of the low frequency component f _j and the high frequency component g _j at each decomposition level were examined. As a result, it was found that the waveform component due to the blink operation can be extracted in most cases by decomposing to level (−4).
[0016]
That is,
[Equation 9]

It was confirmed by experiments that the brain wave component due to blinking can be captured at f- ₄ .
[0017]
FIG. 1 shows the waveform of the original electroencephalogram signal f ₀ used in the experiment. In this waveform, in addition to the normal brain wave component due to intracerebral activity, the brain wave component due to the blink operation is superimposed in the range indicated by reference numeral 1. FIG. 2 shows waveforms of a low frequency component f ₋₁ and a high frequency component g ₋₁ of level (−1) obtained by decomposing the signal f ₀ shown in FIG. 1, and FIG. 3 decomposes f ₋₁ . The waveform of the low-frequency component f ₋₂ and the high-frequency component g ₋₂ of level (−2) obtained in this way is shown. Similarly, FIG. 4 shows the waveform of each component (ie, f ₋₃ , g ₋₃ , f ₋₄ , and g ₋₄ ) at level (−3) and FIG. 5 at level (−4). In the waveform of the low frequency component f _{-4 at} the level (-4) shown in FIG. 5A, the portion corresponding to the range of the code 1 of the original signal in FIG. 1 is sufficiently smooth. The waveform of the electroencephalogram component by eye movement is represented.
[0018]
As described above, by performing preliminary experiments, the electroencephalogram time-series data measured from an electrode mounted at an arbitrary position on the frontal pole is decomposed to level (−4) using a spline 4 function for the mother wavelet. It was found that the electroencephalogram component generated by the blink operation can be extracted. A blink component tends to appear in the signal of the frontal electrode.
[0019]
FIG. 6 is a diagram schematically showing a waveform portion due to the blink operation in the waveform of f- ₄ shown in FIG. As shown in FIG. 6, generally, at the time of blinking, the f- ₄ component generally rises sharply in the positive direction, then falls in the negative direction, and then gradually settles after standing in the positive direction. Draw a trajectory. In the experiment, it was confirmed that there was only one drop in the negative direction per blink for the various EEGs used. Therefore, it is conceivable to obtain blink information by detecting a portion that falls in the negative direction indicated by reference numeral 2 in FIG. In the experiment, if the blink operation continues for a short period of time, the shape of the blink waveform may be broken from the typical waveform as shown in FIG. 6, but the principle is that the blink direction is only once per blink. It was also confirmed that it will not collapse.
[0020]
It was also confirmed by experiment that the amplitude of the electroencephalogram component due to blinking is larger than the amplitude of electroencephalogram due to normal brain activity (other than blinking).
[0021]
That is, as shown in FIG. 7, the portion 5 corresponding to one blink in the f ₋₄ component always has one downward slope portion 4 and the portion where the f ₋₄ component swings negatively, The absolute value of the peak is larger than the absolute value of the average level 3 of the negative value of the electroencephalogram signal.
[0022]
Accordingly, in the present embodiment, the slope of the low frequency component f- ₄ of the level (-4) wavelet decomposition result is negative, and the absolute value of f- ₄ is greater than the average of the electroencephalogram amplitude by a predetermined ratio α or more. At the time, it was determined that blink action occurred. The reason why the ratio is equal to or greater than the predetermined ratio is that a margin for preventing erroneous determination is taken into consideration.
[0023]
For this reason, the average of the values X satisfying X (i) <0 among the time series data X (i) (i is time) of the electroencephalogram detected by the electrode attached to the frontal pole is first obtained.
[0024]
[Expression 10]

Here, N is the number of data taken out to obtain this average value.
[0025]
Then, when the following two conditional expressions (11) and (12) are satisfied at the same time, it is determined that the blink operation is performed.
[0026]
[Expression 11]

[Expression 12]

The above is referred to as a blink operation algorithm. This means that when the inclination of f ₋₄ is negative and the value thereof is α times the average amplitude of the electroencephalogram when the eye is closed, it is determined that the waveform of the blink operation has been captured.
[0027]
The differential on the right side of the equation (12) is expressed as follows:
[Formula 13]

Ask for.
[0028]
Here, the coefficient α can be selected by the user looking at the state of the display waveform. Experiments have shown that it is desirable to limit the selection range of α to 1.4 ≦ α ≦ 2.0. In this way, the range of α is widened because there is an individual difference in the amplitude of the electroencephalogram waveform when the eye is closed, and there is also an individual difference in the amplitude of the blink waveform. This is because even if it can be applied to one person, it may not be applicable to another person.
[0029]
Further, the lower limit 1.4 and the upper limit 2.0 of the selection range of α were determined by experiments. That is, from analysis of many cases, if α is set to a value smaller than 1.4, there is a possibility that an electroencephalogram waveform caused by normal intracerebral activity that is not blink movement may be erroneously determined as blink movement. I understood. It has also been found that if α is set to a value larger than 2.0, a detection omission may occur when the blink waveform is small. From the above, it was determined that the blink operation occurred when both the formulas (11) and (12) were satisfied, and the time when both formulas were satisfied was determined as the blink occurrence time.
[0030]
FIG. 8 shows a schematic configuration of the apparatus of the present embodiment. The apparatus includes an electroencephalograph 10, an electroencephalogram analysis display processing unit 20, and a display device 30. The electroencephalograph 10 is a conventional general electroencephalograph. The electroencephalogram analysis display processing unit 20 performs an analysis on the blink operation on the electroencephalogram signal (time-series data) obtained by the electroencephalograph 10, and generates display image information indicating the analysis result and the electroencephalogram waveform. The display device 30 is a display device such as a CRT or a liquid crystal display that displays the display image information generated by the electroencephalogram analysis display processing unit 20.
[0031]
FIG. 9 is a flowchart showing the procedure of blink operation analysis of the electroencephalogram analysis display processing unit 20 of this apparatus. Hereinafter, the configuration and operation of the apparatus according to the present embodiment will be described with reference to FIGS.
[0032]
The electrodes of the electroencephalograph 10 are, for example, Fp1, Fp2 at the frontal pole, F3, Fz, F4 at the frontal, F7, F8 at the lower frontal, C3, Cz, C4 at the central front, P3, Pz, P4 at the parietal, Wear O1, O2, T3 in the temporal region, T5, T6 in the rear temporal region, and A1, A2 in the earlobe. This method is the 10/20 method recommended by the International Electroencephalographic Society as a standard method. However, this electrode mounting method is merely an example, and the method of this embodiment does not basically depend on the electroencephalogram electrode mounting method. For example, if a new method appears in the future, the electrode may be mounted by that method.
[0033]
The raw electroencephalogram signal detected by the electroencephalograph 10 is amplified and A / D converted into time-series data, and is input to the display image generation unit 26 and the wavelet decomposition unit 22 of the electroencephalogram analysis display processing unit 20 (FIG. 9 S100).
[0034]
The wavelet decomposition unit 22 performs wavelet transformation on the input brain wave time-series data using a spline 4 function as a mother wavelet (S102), wavelet decomposes to level (-4), and generates an electroencephalogram component due to blinking. Separated from the electroencephalogram component of normal brain activity (S104). This decomposition result is sent to the blink analysis unit 24. The blink analysis unit 24 extracts the low-frequency component f- ₄ of level (-4) from the received signal group of the wavelet decomposition result (S106), and formulas (11) and (12) for the extraction result. It is determined whether or not the indicated blink determination condition is satisfied (S108). If this determination result is Yes, it is determined that there is a blink operation, and if it is No, it is determined that there is no blink operation.
[0035]
In this procedure, the time when the blink determination condition is satisfied is set as the occurrence time of the blink operation. Further, the blink analysis unit 24 continuously performs this determination on the time series data of a series of electroencephalogram measurement results, and counts the number of times the blink determination criterion is satisfied, thereby generating a blink generated during the measurement. The cumulative number of eye movements can be obtained. Furthermore, the number of blinking operations per predetermined unit time interval can be counted. If the unit time interval can be set by the user, an analysis result corresponding to the user's purpose is obtained.
[0036]
Information on the blink occurrence time, the cumulative number of occurrences, and the number of occurrences per unit time obtained by the blink analysis unit 24 in this way is passed to the display image generation unit 26. The display image generation unit 26 synthesizes the received blinking time and frequency information with the waveform display image of the electroencephalogram signal data received from the electroencephalograph 10 and supplies the synthesized image to the display device 30. The combination of the blink analysis result and the display result of the electroencephalogram signal is preferably performed so as to match the time phase according to the processing delay in the blink analysis unit 24.
[0037]
With such a configuration, the user can obtain information on the occurrence of the blink operation, the number of times thereof, and the like in addition to the brain wave waveform that changes every moment by the display of the display device 30. Moreover, in this apparatus, information relating to the blinking operation can be obtained simply by attaching the electrode of the electroencephalograph 10 to the subject. Therefore, since it is not necessary to attach a dedicated detection device for detecting blinking motion to the subject, the burden on the subject is reduced.
[0038]
In the above example, the electrodes of the electroencephalograph are attached to the entire head, but only the electrodes of the frontal pole and the reference potential may be used for the blink operation detection only.
[0039]
The blink movement analysis of the present embodiment described above can be applied in real time to an electroencephalogram signal detected every moment by the electroencephalograph 10, or an electroencephalogram measured by the electroencephalograph 10 in the past and stored in a storage device. Time series data can also be extracted and applied to it. In the case of real-time processing, if the blink analysis processing takes a long time, the real-time property of the waveform displayed on the display device 30 may be impaired. However, if a high-speed personal computer is used, it is practical. Analysis can be done in a time that does not cause any problems.
[0040]
The electroencephalogram analysis display processing unit 20 can be implemented in software using a computer system such as a personal computer as a platform. In this case, the computer system may be executed with a program describing the processing procedure described above. This program can be provided from a vendor in the form of a portable recording medium such as a CD-ROM. The user can configure the electroencephalogram analysis display processing unit 20 by installing the program on the recording medium into his computer system.
[0041]
The preferred embodiment of the present invention has been described above, but this is only an example, and various modifications are included in the scope of the present invention.
[0042]
For example, in the above example, the spline 4 function is used for the wavelet decomposition mother wavelet, but other mother wavelet functions can also be used. When the spline 4 function is used, the electroencephalogram component due to the blink operation can be extracted by the decomposition up to the level (−4). However, when other functions are used, it is necessary to determine the number of decomposition levels accordingly. is there. What level of decomposition should be obtained by experiments or the like, and the setting of the number of wavelet decomposition levels of the wavelet decomposition unit 22 may be changed accordingly.
[0043]
Moreover, the blink determination conditions illustrated are examples, and determination conditions other than this can also be used.
[0044]
【The invention's effect】
As described above, according to the present invention, it is possible to extract information related to the blink action from the electroencephalogram measurement result. Therefore, only by attaching the brain wave detection device to the subject, information on the blink action as well as the brain wave can be obtained. Can be obtained and the burden on the subject can be reduced.
[Brief description of the drawings]
FIG. 1 is a diagram showing a waveform of original electroencephalogram data including a component caused by blinking.
FIG. 2 is a diagram showing waveforms of a low frequency component f-1 and a high frequency component g-1 at level (-1) obtained by wavelet decomposition of original data.
FIG. 3 is a diagram illustrating a waveform of a decomposition result of level (−2).
FIG. 4 is a diagram showing a waveform of a decomposition result of level (−3).
FIG. 5 is a diagram showing a waveform of a decomposition result of level (−4).
FIG. 6 is a diagram for explaining the characteristics of a blink waveform.
FIG. 7 is a diagram for explaining determination conditions for a blink waveform.
FIG. 8 is a diagram illustrating a device configuration of the embodiment.
FIG. 9 is a flowchart showing a procedure of blink analysis processing.
[Explanation of symbols]
10 electroencephalograph, 20 electroencephalogram analysis display processing unit, 22 wavelet decomposition unit, 24 blink blink analysis unit, 26 display image generation unit, 30 display device.

Claims (6)

An electroencephalogram detection means;
Decomposition means for performing wavelet decomposition on time series data of the electroencephalogram detected by the electroencephalogram detection means to a predetermined decomposition level;
From the low frequency component in the decomposition result of the predetermined decomposition level obtained by the decomposition means, the blink analysis means for obtaining information about blink operation,
It has a, the inclination of the low frequency component in the decomposition result of a predetermined division level is negative, and the average of the alpha times (alpha is a constant) of the negative of the value is the electroencephalogram time series data of the low frequency component value less than A blink movement analysis apparatus characterized by determining whether or not there is a blink action under the blink determination condition . Blinking motion analysis device according to claim 1, characterized in that it comprises a means for accepting a setting of the constant α from the user. By counting the number of times the blink determination condition by analyzing the EEG time series data is satisfied, blinking motion analysis device according to claim 1, characterized in that it comprises means for determining the number of blinking operation . By analyzing the EEG time series data, according to claim 1, characterized in that it comprises means for determining by the frequency of the blinking operation counting the number of times the blink determination condition is satisfied within a predetermined unit time The blink operation analysis device described. The decomposition means uses a spline 4 functions as the mother wavelet, blinking operation according to any one of claims 1 to 4, characterized in that performing wavelet decomposition to a level (-4) as the predetermined resolution level Analysis device. Acquiring time series data of an electroencephalogram output from the electroencephalogram detection means ;
Wavelet decomposition of the detected electroencephalogram time-series data to a predetermined decomposition level;
Obtaining information on blink movement from a low frequency component in the decomposition result of the predetermined decomposition level of the electroencephalogram time-series data;
Only including, a slope of the low-frequency component is negative in the degradation results of the serial predetermined division level, and the average of the alpha times (alpha is a constant) of the negative of the value is the electroencephalogram time series data of the low frequency component value less than A blink movement analysis method, wherein the presence or absence of blink movement is determined under the condition of blink determination .

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