Abstract-Significant advances in neuroscience, sensor technologies, and efficient signal processing algorithms have greatly facilitated the transition from laboratory-oriented neuroscience research to practical applications. Brain-computer interfaces (BCIs) represent major strides in translating brain signals into actionable decisions and primarily consist of hardware and software that guide the communications between users and systems. This article presents several current neurotechnologies and computational intelligence methods applied to EEG-based BCIs. In the hardware aspect, novel portable EEG devices featuring dry electrodes are introduced as substitutes for traditional BCIs with wet electrodes and its bulky size. With these advantages, these novel EEG devices can acquire real-time EEG signals for operational workplaces without requiring conductive gel/paste or scalp preparations. As for the software aspect, blind source separation, artificial neural networks, effective connectivity measurements and information fusion techniques are introduced to address the technical issues of artifact removal, rapid event-related potential detection, complex brain network description, and decision fusion, respectively. For instance, information fusion technique has been utilized to attack the individual differences problem of motor imagery applications in the real-world environment. With continuous improvements in the development of a convenient approach to record brain signals and extract knowledge regarding intentions, BCI techniques are envisioned to lead to a wide range of real-life applications in the near future.