Joint EEG — EMG signal processing for identification of the mental tasks in patients with neurological diseases

Correlation size together with Lyapunov exponents estimated from both electroencephalography (EEG) and electromyography (EMG) signals, are the crucial variables in the classification of mental tasks using an artificial neural network (ANN) classifier for patients suffering from neurological disorders/diseases. The above parameters vary according to the status of the patient, for example: depending on how stressed or relaxed the patient is and what mental task is executed. The signals were acquired from patients with Parkinson disease, while they performed four different mental tasks. The performed mental states, detected with high specificity and accuracy, can help a completely paralyzed person (locked-in) to communicate with the environment through the brain waves, leading to increasing their quality of life.