| dc.description.abstract | Music recommendation systems based on emotions have gained traction for their
ability to personalize user experiences. However, such systems often overlook the
unique needs of blind individuals by depending heavily on visual user interfaces and
data inputs such as facial expressions. This research systematically reviews existing
emotion-based music recommendation systems, employing the PRISMA methodology to
analyze their suitability for blind users. It explores various emotion detection methods,
including electroencephalogram (EEG) signals, voice commands, and physiological
signals, emphasizing non-visual alternatives. This study focuses on evaluating current
technologies and methods, identifying their strengths and limitations, and proposing
hybrid solutions combining EEG and voice recognition. This review results in the
identification of significant gaps in accessibility and precision in existing systems for
blind users. The proposed technique integrates EEG-based emotion detection and voice command systems to create a non-visual, user-centered music recommendation platform.
This hybrid approach leverages real-time adaptability and artificial intelligence-driven
personalization to address these challenges. It will enhance the inclusivity and
emotional engagement of blind users, providing accurate emotion detection and
seamless interaction. The implications of the study highlighted the advancements
in hybrid technologies and artificial intelligence are vital for future development to
bridge the accessibility gap and ensure equitable user experiences. | en_US |
