Currently, the concept of BCIs is under intense study.BCIs extract activity from the human brain as cranial nerve information and use the information as inputs to control machinery and equipment.If this could enable the operation of machinery and equipment directly from cranial nerve information without the subject moving the hands and feet, such systems could be applied to care-taking robots for physically handicapped individuals.BCI systems can be divided into two types.The invasive type reads cranial nerve information using electrodes embedded directly into the brain.The non-invasive type reads cranial nerve activity from the surface of the head using NIRS or electroencephalography (EEG).NIRS imposes fewer restrictions on body movement than EEG and is more resistant to electronic noise, so the load imposed on the user is less and electronic devices have no influence.The present study therefore focused on BCIs using NIRS (NIRS-BCI).In this study, we assume artificial arm for physically handicapped individuals and propose a real-time NIRS-BCI that enables operation of robot arm using NIRS.Furthermore, we propose a new brain activity judgment method using neural network, to achieve highly accurate on/off operations.With a judgment method that used a simple threshold, judgment of brain activity was unstable.The proposed judgment method that uses oxyHb and its derivative selects the most effective channel automatically, and makes a judgment using the neural network.Experimental results showed that the proposed method enabled more accurate and stable judgments than the method that uses a simple threshold.