Artificial Intelligence (AI) in Neurofeedback Therapy Using Electroencephalography (EEG), Heart Rate Variability (HRV), and Galvanic Skin Response (GSR): A Review

Attention problems, such as attention deficit hyperactivity disorder (ADHD), have a significant impact on quality of life and academic achievement. Using Electroencephalogram (EEG), Heart Rate Variability (HRV), and Galvanic Skin Response (GSR), this literature review analyzes the use of Artificial Intelligence (AI) in Neurofeedback Therapy (NFT). The proposed framework, detailed and illustrated in AI-powered NFT applications, emphasizes AI’s pivotal role in preprocessing data, feature extraction, and AI model analysis, enabling dynamic adjustment of therapy protocols based on real-time physiological responses and facilitating remote therapy via Cloud AI. Although artificial intelligence has begun to be integrated into NFTs, its use is still in its early stages and presents a few challenges. The lack of representative datasets for training AI models, the complexities of processing biometric data (EEG, HRV, GSR), and the high infrastructure costs required for implementation are the key barriers to widespread adoption. Additionally, the general adoption of this technology is restricted by concerns regarding patient security and data privacy. The paper emphasizes the role of artificial intelligence in preprocessing data, feature extraction, and AI model analysis by integrating current research findings. The authors propose a novel approach to enhance the efficacy of neurofeedback therapy by integrating artificial intelligence with EEG, HRV, and GSR data, even though most of the content is derived from previous research. The researchers achieved substantial results by employing CNN, SVM, and LSTM to identify EEG, HRV, and GSR signals. However, challenges such as overfitting, data noise, and ethical concerns persist. The objective of this review is to offer a fair evaluation of the current state of artificial intelligence in NFT, identify gaps in the literature, and propose future research directions. These directions include the development of robust AI algorithms, the integration of NFT with emerging technologies such as virtual reality, ethical and societal implications, and AI-powered NFT applications. Additionally, this review proposes a framework for the integration of AI into NFT. AI-powered NFT has the potential to revolutionize the treatment of attention disorders and enhance the outcomes for those who are affected by surmounting these obstacles.