EEG-Emotion-Analysis

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Description:

EEG emotion detection is a technique that uses electroencephalography (EEG) to measure brain activity and identify different emotional states. EEG is a non-invasive method that measures electrical activity in the brain through electrodes placed on the scalp. The electrical activity of the brain is related to different cognitive and emotional processes, and EEG can be used to measure changes in brain activity that occur in response to different emotions.

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• The dataset you provided is a dataset of EEG signals that have been labeled with the corresponding emotional state. The dataset contains 1000 trials, each of which is a 4 second EEG signal. The emotional states that are represented in the dataset are happiness, sadness, anger, and fear.

The features that are included in the dataset are:

• Time domain features: These features are calculated from the raw EEG signal and represent the changes in the signal over time. Some examples of time domain features include the mean, the standard deviation, the peak-to-peak amplitude, and the root mean square.
• Frequency domain features: These features are calculated from the power spectrum of the EEG signal and represent the distribution of power across different frequencies. Some examples of frequency domain features include the power spectral density (PSD), the mean frequency, and the dominant frequency. Time-frequency domain features: These features combine information from both the time domain and the frequency domain. Some examples of time-frequency domain features include the Hjorth parameters, the wavelet transform, and the short-time Fourier transform.
• Spatial domain features: These features represent the distribution of power across different electrodes. Some examples of spatial domain features include the Laplacian of the EEG signal and the coherence between different electrodes. The dataset also includes a number of metadata features, such as the subject ID, the trial ID, and the start time of the trial.

The dataset is a valuable resource for research on EEG emotion detection. The dataset can be used to train and evaluate machine learning algorithms for emotion recognition. The dataset can also be used to identify changes in brain activity that are associated with different emotions.

Here are some additional details about the features in the dataset:

Time domain features:

• Mean: The average value of the EEG signal over time.
• Standard deviation: The standard deviation of the EEG signal over time.
• Peak-to-peak amplitude: The difference between the maximum and minimum values of the EEG signal over time.
• Root mean square: The square root of the mean of the squared values of the EEG signal over time.

Frequency domain features:

• Power spectral density (PSD): The power of the EEG signal as a function of frequency.
• Mean frequency: The average frequency of the EEG signal.
• Dominant frequency: The frequency of the EEG signal with the highest power.

Time-frequency domain features:

• Hjorth parameters: A set of three parameters that characterize the shape of the EEG signal's power spectrum.
• Wavelet transform: A mathematical transform that decomposes the EEG signal into a series of wavelets.
• Short-time Fourier transform: A mathematical transform that decomposes the EEG signal into a series of frequency components over a short time window. Spatial domain features:
• Laplacian of the EEG signal: A measure of the spatial variation of the EEG signal.
• Coherence between different electrodes: A measure of the correlation between the EEG signals at different electrodes.