An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Abstract

Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3–22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

“…Both the last column and second to last column of Table 1 are of particular importance in this work, as their clear definition and clinical setting allow for the reproducibility of the results, and facilitate data sharing and collaboration. In addition to the listed studies, there is an achievement of binary classification for the valence axis to detect happy or sad human emotional states using only two channels (FP1 and FP2) with a high efficiency of 97.42% [39]. It states that the most reliable performance for emotion recognition is obtained using the three frequency bands which are delta, alpha, and gamma.…”

State-of-the-Art on Brain-Computer Interface Technology

“…Additionally, some algorithms are able to identify distinct features within each individual's recordings, allowing personalized treatment approaches [55,56]. Finally, artificial intelligence techniques are being explored that could help automate certain aspects of processing raw data, resulting in faster diagnostic times [57][58][59][60]. collected from EEG recordings can also be analyzed to assess cognitive abilities such as attention span or memory recall speed.…”

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

“…Nowadays, AI is being utilized in many different fields, with endless applications including prediction, Internet-of-Things (IoT), classification, detection, recognition, etc. [15][16][17][18][19][20]. The AI technique that we employed here is Deep Reinforcement Learning (DRL), a promising approach in handling advanced control problems [21][22][23].…”