This paper investigates the development of an intelligent system method to address completely locked-in-syndrome (CLIS) that is caused by some illnesses such as Amyotrophic Lateral Sclerosis (ALS) as the most predominant type of Motor Neuron Disease (MND). In the last stages of ALS and despite the limitations in body movements, patients however will have a fully functional brain and cognitive capabilities and able to feel pain but fail to communicate. This paper aims to address the CLIS problem by utilizing EEG signals that human brain generates when thinking about a specific feeling or imagination as a way to communicate. The aim is to develop a low-cost and affordable system for patients to use to communicate with carers and family members. In this paper, the novel implementation of the ASPS (Automated Sensor and Signal Processing Selection) approach for feature extraction of EEG is presented to select the most suitable Sensory Characteristic Features (SCFs) to detect human thoughts and imaginations. Artificial Neural Networks (ANN) are used to verify the results. The findings show that EEG signals are able to capture imagination information that can be used as a means of communication; and the ASPS approach allows the selection of the most important features for reliable communication. This paper explains the implementation and validation of ASPS approach in brain signal classification for bespoke arrangement. Hence, future work will present the results of relatively high number of volunteers, sensors and signal processing methods.