Initial investigation on alpha asymmetry during listening to therapy music

Several studies had shown that frontal alpha-asymmetry is closely related to emotions and motivation. The present study is to investigate the frontal alpha asymmetry during listening to the therapy musical piece. Electroencephalograph (EEG) was used to investigate the emotions states of relax, tense, sad or not focus. 10 participants were involved in this study. Mean power of both hemispheres was computed and the Asymmetry Relation Ratio (ARR) was calculated. From the calculation, individuals with relatively positive ratio shows more relax than the negative ratio.

[1]  B. Lithgow,et al.  Alpha-band characteristics in EEG spectrum indicate reliability of frontal brain asymmetry measures in diagnosis of depression. , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[2]  Jeffrey B. Henriques,et al.  Left frontal hypoactivation in depression. , 1991, Journal of abnormal psychology.

[3]  Yuan-Pin Lin,et al.  EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Y. Mitsukura,et al.  The EEG analysis method for obtaining the feeling , 2008, 2008 International Conference on Control, Automation and Systems.

[5]  G. Dawson,et al.  Frontal electroencephalographic correlates of individual differences in emotion expression in infants: a brain systems perspective on emotion. , 1994, Monographs of the Society for Research in Child Development.

[6]  M. Kostyunina,et al.  Frequency characteristics of EEG spectra in the emotions , 1996, Neuroscience and Behavioral Physiology.

[7]  É. Labbé,et al.  Coping with Stress: The Effectiveness of Different Types of Music , 2007, Applied psychophysiology and biofeedback.

[8]  H Hinrichs,et al.  Basic emotions reflected in EEG-coherences. , 1992, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[9]  Toshimitsu Musha,et al.  Feature extraction from EEGs associated with emotions , 1997, Artificial Life and Robotics.

[10]  Simon Hanslmayr,et al.  Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli , 2009, Neuroscience Letters.

[11]  Jan Kaiser,et al.  Subjective mood estimation co-varies with spectral power EEG characteristics. , 2008, Acta neurobiologiae experimentalis.

[12]  Yasue Mitsukura,et al.  Method for detecting music to match the user’s mood in prefrontal cortex electroencephalogram activity based on individual characteristics , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[13]  E. Bekiaris,et al.  Using Spectral Analysis to Extract Frequency Components from Electroencephalography: Application for Fatigue Countermeasure in Train Drivers , 2007, The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007).

[14]  L. Trainor,et al.  Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions , 2001 .

[15]  Hung-Wen Chiu,et al.  Discovering EEG Signals Response to Musical Signal Stimuli by Time-frequency analysis and Independent Component Analysis , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[16]  Yasue Mitsukura,et al.  Extraction of EEG characteristics while listening to music and its evaluation based on a latency structure model with individual characteristics , 2009 .