Since the first recordings of brain electrical activity more than 100 years ago remarkable contributions have been done to understand the brain functionality and its interaction with environment. Regardless of the nature of the brain-computer interface BCI, a world of opportunities and possibilities has been opened not only for people with severe disabilities but also for those who are pursuing innovative human interfaces. Deeper understanding of the EEG signals along with refined technologies for its recording is helping to improve the performance of EEG based BCIs. Better processing and features extraction methods, like Independent Component Analysis (ICA) and Wavelet Transform (WT) respectively, are giving promising results that need to be explored. Different types of classifiers and combination of them have been used on EEG BCIs. Linear, neural and nonlinear Bayesian have been the most used classifiers providing accuracies ranges between 60% and 90%. Some demand more computational resources like Support Vector Machines (SVM) classifiers but give good generality. Linear Discriminant Analysis (LDA) classifiers provide poor generality but low computational resources, making them optimal for some real time BCIs. Better classifiers must be developed to tackle the large patterns variability across different subjects by using every available resource, method or technology.

PD patients come to experience a wide spectrum of cognitive, psychiatric, non-motor, and motor symptoms. Tremor is one of the initial symptoms described by 70% of patients diagnosed with PD and quantifying it will allow the specialist to know the evolution of his patient and the posible treatment to implement. The objective of this research is to identify systems, devices or instruments and the methods used to monitor tremors that affect the upper extremities of patients with PD. In addition, this work proposes an initial prototype system that allows the capture of involuntary movements that occur in patients with PD. It was observed that the largest number of articles use their monitoring devices in controlled environments and despite the fact that there are many studies that are developing devices for monitoring tremors in the upper extremities, more efforts are still needed in this area. Many studies conclude that using IMU sensors has proven to be a good technique that allows monitoring of tremors. Other results obtained show that the proposed prototype works correctly, since when compared with a commercial application on a cell phone, very similar values are obtained, demonstrating its proper functioning.

Since the first recordings of brain electrical activity more than 100 years ago remarkable contributions have been done to understand the brain functionality and its interaction with environment. Regardless of the nature of the brain-computer interface BCI, a world of opportunities and possibilities has been opened not only for people with severe disabilities but also for those who are pursuing innovative human interfaces. Deeper understanding of the EEG signals along with refined technologies for its recording is helping to improve the performance of EEG based BCIs. Better processing and features extraction methods, like Independent Component Analysis (ICA) and Wavelet Transform (WT) respectively, are giving promising results that need to be explored. Different types of classifiers and combination of them have been used on EEG BCIs. Linear, neural and nonlinear Bayesian have been the most used classifiers providing accuracies ranges between 60% and 90%. Some demand more computational resources like Support Vector Machines (SVM) classifiers but give good generality. Linear Discriminant Analysis (LDA) classifiers provide poor generality but low computational resources, making them optimal for some real time BCIs. Better classifiers must be developed to tackle the large patterns variability across different subjects by using every available resource, method or technology.