Wavelet-transformed EEG signal recordings categorized by time-frequency characteristics for alcohol detection. The data provides processed neural features from multiple electrode channels to distinguish between alcoholic and control subjects.
Use Cases
- Train a classification model to identify alcohol consumption patterns using wavelet-transformed EEG coefficients
- Analyze time-frequency characteristics of neural signals to determine the physiological effects of alcohol
- Compare the efficacy of different wavelet decomposition levels for feature extraction in EEG-based diagnostics
Strengths
- Includes wavelet-transformed coefficients derived from raw EEG signals
- Features time-frequency domain characteristics specifically for alcohol detection tasks
- Contains labels for alcoholic and control subjects