Emotion recognition using electroencephalographic (EEG) recordings is a new area of research which focuses on recognition of emotional states of mind rather than impulsive responses. EEG recordings are found useful for the detection of emotions through monitoring the emotion characteristics of spatiotemporal variations of activations inside the brain. To distinguish between different emotions using EEG data, we need to provide specific spectral descriptors as features to quantify these spatiotemporal variations. We propose several new features, namely Normalized Root Mean Square (NRMS), Absolute Logarithm Normalized Root Mean Square (ALRMS), Logarithmic Power (LP), Normalized Logarithmic Power (NLP), and Absolute Logarithm Normalized Logarithmic Power (ALNLP) for the classification of emotions. A protocol has been established to elicit five distinct emotions: joy, sadness, disgust, fear, surprise, and neutral. EEG signals are collected using a 256-channel system, preprocessed using band-pass filters and a Laplacian Montage, and decomposed into five frequency bands using Discrete Wavelet Transform. The decomposed signals are transformed into different spectral descriptors and are classified using a two-layer Multilayer Perceptron (MLP) neural network. The Logarithmic Power descriptor produces the highest recognition rates, 91.82% and 94.27% recognition for two different experiments, which is more than 2% higher than when using other features.