Abstract
A neurofeedback system adjusting an individual's attention is an effective treatment for attention-deficit/hyperactivity disorder (ADHD). In current studies, an accurate measure of the level of human attention is one of the key issues that arouse much interest. This paper proposes a novel optimized complex network method (OCNM) for measuring an individual's attention level using single-electrode electroencephalography (EEG) signals. A time-delay embedding algorithm was used to reconstruct EEG data epochs into nodes of the OCNM network. Euclidean distances were calculated between each two nodes to decide edges of the network. Three key parameters influencing OCNM, i.e., delaying time, embedding dimension, and connection threshold, were optimized for each individual. The average degree and clustering coefficient of the constructed network were extracted as a feature vector and were classified into two patterns of concentration and relaxation using an LDA classifier. In the offline experiments of six subjects, the classification performance was tested and compared with an attention meter method (AMM) and an α + β + δ + θ + R method. The experimental results showed that the proposed OCNM achieved the highest accuracy rate (80.67% versus 70.58% and 68.88%). This suggests that the proposed method can potentially be used for EEG-based neurofeedback systems with a single electrode.
Highlights
A neurofeedback system aiming at building the selfregulation mechanism is commonly used to adjust an individual’s brain activity by means of biofeedback
It is an effective treatment for attention-deficit/hyperactivity disorder (ADHD) which is a common disorder in psychiatry with a worldwide prevalence of approximately 5.2% [1]. e major symptom of ADHD is lack of attention, and measuring the human attention is one of the key issues in current researches of the neurofeedback systems
We propose a novel optimized complex network method (OCNM) based on nonlinear time series analysis to measure an individual’s attention level. e network is constructed from the single-electrode EEG signals using parameters optimized for each individual, and the average degree and the average clustering coefficient are extracted as features for classification
Summary
A neurofeedback system aiming at building the selfregulation mechanism is commonly used to adjust an individual’s brain activity by means of biofeedback. It is an effective treatment for attention-deficit/hyperactivity disorder (ADHD) which is a common disorder in psychiatry with a worldwide prevalence of approximately 5.2% [1]. Losing attention usually produces changes in the EEG signals of theta (4–8 Hz) and beta (13–20 Hz) bands. Amplitudes of these frequency bands were extracted from an FPz electrode on the forehead to assess the subject’s attention level [3]. Various methods using data from multiple electrodes, such as relative power spectrum method and independent component analysis (ICA), have been proposed for improving the performance of measuring attention [4]
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