The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0–70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.

This work analyzes several feature extraction methods used in today’s EEG BCI (electro-encephalogram brain computer interface) classification systems. Comparison of multiple EEG energy algorithms is presented for solving a 4-class motor imagery BCI classification problem. Furthermore, multiple feature vector generation techniques are employed into analysis. The effectiveness of CSP (common spatial pattern) filtering method in preprocessing step is shown. Channel difference feature extraction method is presented. It is discussed that key aim in today’s EEG signal analysis should be dedicated to finding more accurate techniques for determining better quality features. Initial tests prove that static feature extraction methods are not optimal and adaptive algorithms are required to overcome subject specific EEG signal variations. Further work and new dynamic feature extraction methods are required to solve the problem.

This paper presents the design and implementation of a compact in size, modular battery powered conventional EEG (cEEG) signal acquisition board based on ADS1298. An introduced novel vertically stackable board design allows solve EEG front-end scaling problem and to effectively reconfigure hardware even for most demanding applications. The system allows to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over Bluetooth or Wi-Fi interface. Furthermore, simple but effective validation techniques are provided for such system evaluation. Conducted tests confirm the validity of the system for real world applications and against official datasheet specifications. With a 6.59 microvolts peak-to-peak input referred noise and -97 dB common mode rejection ratio in 0-70 Hz band the proposed design can be a low-cost precision cEEG research device.