PROBLEM TO BE SOLVED: To execute highly accurate sleepiness determination with a high real-time property at a low cost by discriminating a section in which noises are included from a section in which noises are fewer using only brain waves directly reflecting the brain activity.SOLUTION: Brain wave data in a time zone detected by a brain wave detection part 2 are converted to data in a frequency zone by a frequency analysis part 12, and powers in a δ zone, θ zone, α zone, and β zone respectively are found from power spectra obtained from the data in the frequency zone. Then, a myoelectric detection part 13 detects generated myoelectricity from the power in the β zone, and a sleepiness index calculation part 14 finds a sleepiness index from the powers in the δ zone, θ zone, and α zone respectively. If there is no myoelectricity, sleepiness is determined from the sleepiness index SL and sleepiness information is output from a sleepiness determination part 15. If there is myoelectricity, high wakefulness information is output. In this way, the section including noises can be discriminated from the section with fewer noises by using brain waves only, and the low-cost and highly accurate determination of sleepiness with a high real-time property is possible.COPYRIGHT: (C)2013,JPO&INPIT【課題】脳活動を直接的に反映している脳波のみを用いて雑音の含まれている区間と雑音の少ない区間を識別し、低コストでリアルタイム性の高い、高精度な眠気判定を行う。【解決手段】脳波検出部2で検出した時間領域の脳波データを周波数分析部12で周波数領域のデータに変換し、周波数領域のデータから得られるパワースペクトルからδ帯域,θ帯域,α帯域,β帯域のそれぞれのパワーを求める。そして、筋電検出部13でβ帯域のパワーから筋電発生を検出し、眠気指標算出部14でδ,θ,α帯域の各パワーから眠気指標を求め、「筋電無し」の場合、眠気指標SLから眠気を判定して眠気判定部15から眠気情報を出力し、「筋電有り」の場合、高覚醒の情報を出力する。これにより、脳波のみを用いて雑音の含まれている区間と雑音の少ない区間を識別することができ、低コストでリアルタイム性の高い高精度な眠気判定が可能となる。【選択図】図1
