The rise of artificial intelligence and intense learning has further propelled the development of MER, with deep-learning-based approaches becoming increasingly prevalent. Furthermore, electroencephalography (EEG), a non-invasive technique for measuring brain activity, has emerged as a valuable tool for exploring emotions through brain-computer interfaces. The paper surveys EEG-based music emotion analysis, focusing on the analytical processes involved, such as data processing, emotion modeling, and feature extraction. The paper then identifies key challenges and outlines future development trends in EEG-based music emotion recognition. Finally, the paper provides a concluding summary. Songs serve as a powerful medium for emotional expression for performers and listeners. Leveraging advancements in machine learning and sentiment analysis, the study investigates mood detection based on recently played music [8]. While the Four-Dimensional Mood Scale (FDMS) provides a direct measure of mood across valence and arousal dimensions, the research utilizes a modified Indonesian adaptation, the FDMS-55, for comparative analysis. The researcher developed a predictive model using song data sourced from the Spotify and Genius APIs, manually annotated with mood classifications, and processed using the Azure Cognitive Service Text Analytics API. The model evaluation revealed that the FastTreeOva algorithm achieved the highest accuracy, with scores of 0.8901 for valence and 0.9167 for arousal. Using cosine similarity, a comparison of model predictions with FDMS-55 results yielded a mean similarity of 0.770 with a standard deviation of 0.103. These findings suggest a correlation between an individual's mood and their recent music choices, demonstrating the potential of the developed model to predict mood based on listening history accurately. In [9], the paper presents a music recommendation system that leverages deep learning to select songs based on a user's mood, as determined through facial expression analysis. Finding the right song can be challenging with the ever-growing music volume available. The system addresses this issue by providing personalized playlists based on detected emotions. The system employs image processing and neural networks, utilizing a novel architecture inspired by [PERSON]'s research. Specifically, the multitask cascaded convolution neural network (MTCNN) and FaceNet architecture are used for face detection and recognition, generating embeddings fed into a CNN to predict the user's emotion. This emotion prediction drives a classifier recommending music from a Spotify dataset containing nearly 600,000 songs. Users can specify the desired playlist length and add the recommended songs to their personalized playlist. The proliferation of music production and consumption in the digital age has led to the generation of vast amounts of data, necessitating automated and efficient management strategies [10]. Automated music mood detection has emerged as a prominent task within the music information retrieval (MIR) field. The work investigates and compares single-channel and multi-modal deep-learning approaches for music mood detection. The first approach separately analyzes a musical track's audio signal and lyrics, while the second employs a unified multi-modal analysis to classify the data into mood categories. The MoodyLyrics dataset will be used for training and evaluation, comprising 2000 song titles labeled with four mood classes (happy, angry, sad, and relaxed). The outcome of this research will be a robust system for predicting the overall mood of a music track, with potential applications in various domains. The rapid expansion of online music resources has created challenges in music information retrieval and management, making efficient classification and retrieval a crucial research area [11]. The paper utilizes [PERSON]'s two-dimensional emotion plane as the foundation for constructing a music emotion database.