Using Brain-Computer Interfaces (BCI) as an assistive technology aims at providing an innovative solution adapted to subjects' disabilities. BCI either provide a new interface for controlling solution mobility (e.g. wheelchair) or monitoring the state of user during his/her journey. This would be possible by implementing these interfaces on Embedded Systems (ES). However, because of the BCI sophisticated data processing and the ES limited computation performances, the computation time for a real-time use of the BCI on an ES is a limitation. Hence in this work, we investigate and evaluate the parallelization and acceleration performances, on a Raspberry Pi 2 model B (RPi) board, of an STFT-based algorithm for estimating cognitive workload from an Electroencephalographic (EEG) signal. This is done based on multi-core CPU and GPU architectures of the used RPi. Results show that the parallelized implementation using the CPU runs up to × faster than a simple implementation. Compared to CPU of intel-CORE i3 processor, the GPU of the RPi revealed large difference in computation time.