A time-frequency method based on Cohen’s class of distribution is proposed for analysis of functional magnetic resonance imaging (fMRI) data and to detect activation in the brain regions. The Rihaczek-Margenau distribution among the various distributions of Cohen’s class produces the least amount of cross products and is used here for calculating the spectrum of fMRI time-series. This method also does not suffer from the time and frequency resolution trade-off which is inherent in short-term Fourier transform (STFT). Other than detecting activation, the time-frequency analysis is also capable of providing us with more details about the non-stationarity in fMRI data, which can be used for clustering the data into various brain states. The results of brain activation detection with this techniques are presented here and are compared with other prevalent techniques.