Attention Detection Using EEG Signals and Machine Learning: A Review

Attention detection using electroencephalogram (EEG) signals has become a popular topic. However, there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG signals. Therefore, this survey outlines recent advances in EEG-based attention detection within the past five years, with a primary focus on auditory attention detection (AAD) and attention level classification. First, we provide a brief overview of commonly used paradigms, preprocessing techniques, and artifact-handling methods, as well as listing accessible datasets used in these studies. Next, we summarize the machine learning methods for classification in this field and divide them into two categories: traditional machine learning methods and deep learning methods. We also analyse the most frequently used methods and discuss the factors influencing each technique's performance and applicability. Finally, we discuss the existing challenges and future trends in this field.