This study aimed to develop an emotion recognition model using brain signals. The brain computer interface (BCI) focuses on creating technology that enables direct brain-to-external device connections. There are two forms of BCI: invasive and non-invasive. In BCI, electroencephalography (EEG) is essential. EEG is a non-invasive method that involves applying electrodes to the scalp to capture electrical activity in the brain. EEG data is utilized to decode the user's intended emotions, activities, and thoughts. Emotions are important for human interaction, communication, and overall well-being. Many paralyzed people worldwide are unable to express their emotions or meet their needs, making it difficult to understand them, which leads to feelings of isolation. However, it is possible to detect emotions using BCI. Emotions are reflected in electrical brain activity and can be analyzed using EEG signals. The EEG signals are then decoded to detect a person's respective emotions. The decoding process mainly includes three steps. First, the signals are pre-processed to remove noise, and data is encoded. Second, the relevant features are extracted using the spectral power method. Third, emotions are classified using long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM) algorithms. New EEG data is given to the model, and then emotions are displayed. The model developed using BiLSTM achieved an accuracy of 93.97%. A comparison was made with existing classification techniques that have used many three-dimensional (3D) models and the arousal-valence ratio to identify a person's emotion. The model's generalization will improve further by testing it on different types of datasets. The model's generalization improves further by testing it on different types of datasets.