A comparison between classical and new proposed feature selection methods for attention level recognition in disordered children

Abstract

Lack of attention is a chronic behavior in ADHD (Attention Deficit Hyperactivity Disorder) and ASD (Autism Spectrum Disorder). Our goal is to develop a reliable method for the detection of inattention with high accuracy and low time consumption to be used in real time neurofeedback. The new applied methods for inattention in children are EMD (Empirical Mode Decomposition) with difference time series (Dt) and MRA (Multi Resolution Analysis). EMD is a method of breaking down a signal into ‘modes’ (IMFs) representing its different frequency components. Furthermore, MRA strikes balance between temporal and frequency resolution through localizing the EEG signal in frequency domain of interest (beta range) by wavelet decomposition or EMD and then retains time domain information using FD. As the results demonstrate, in intermediate and severe level cases of inattention, EMD_Dt technique is the most accurate. In mild level cases of inattention MRA (wavelet + FD) technique performance is better than EMD_Dt. However, the time consumption of the MRA (wavelet + FD) technique is fifteen times larger than EMD_Dt technique. EMD_Dt is the best technique as it requires less processing time which is the most important factor in neurofeedback, furthermore, clinician concerned more with severe and intermediate level of inattention to be treated.

Keywords

• Quantitative Electroencephalogram
• Fractal Dimension
• Empirical Mode Decomposition
• Multi Resolution Analysis
• Neurofeedback
• Attention level recognition