Using single point brain wave instrument to explore and verification of music frequency

No abstract is available for this paper.

• Publication year

  2017

• Venue

  2017 International Conference on Innovative and Creative Information Technology (ICITech)

• Publication date

  2017-11-01

• Fields of study

  Medicine, Psychology

• Identifiers
  DOI 10.1109/INNOCIT.2017.8319142
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters.

  2017cited by this paper
• The use of single-electrode wireless EEG in biobehavioral investigations.

  2015cited by this paper
• Competition between recently potentiated synaptic inputs reveals a winner-take-all phase of synaptic tagging and capture

  2014cited by this paper
• Inter‐subject synchronization of brain responses during natural music listening

  2013cited by this paper
• Brain Reading: Decoding Mental States From Brain Activity In Humans

  2011cited by this paper
• Psychological Pressure in Competitive Environments: Evidence from a Randomized Natural Experiment

  2010cited by this paper
• Using Electroencephalography to Measure Cognitive Load

  2010cited by this paper
• This Is Your Brain on Music: The Science of a Human Obsession

  2009cited by this paper
• A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

  2004cited by this paper
• Posttraumatic stress disorder: the burden to the individual and to society.

  2000cited by this paper
• The relationship between subjective preference for sound fields with vocal music and brain activity

  1998cited by this paper

• Affective computing in the context of music therapy: a systematic review

  2021cites this paper
• An Individual Specific Electroencephalography Signal Pattern Verification Model Based on Machine Learning and Convolutional Neural Network

  2020cites this paper