The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine the optimal acquisition frequency. The brain zones implicated in motor imagery movement were analyzed, with the aim of improving wearable-EEG comfort and portability. Two detection algorithms with different configurations were implemented. The detection output was classified using a tool with various classifiers. The results were categorized into three groups to discern differences between general hand movements and no movement; specific movements and no movement; and specific movements and other specific movements (between five different finger movements and no movement). Testing was conducted on the sampling frequencies, trials, number of electrodes, algorithms, and their parameters. The preferred algorithm was determined to be the FastICACorr algorithm with 20 components. The optimal sampling frequency is 1 kHz to avoid adding excessive noise and to ensure efficient handling. Twenty trials are deemed sufficient for training, and the number of electrodes will range from one to three, depending on the wearable EEG’s ability to handle the algorithm parameters with good performance.

In this work, we present autonomous active tags. The power sources of these active tags employ energy harvesting techniques, specifically, solar and mechanical techniques. The integration of these techniques, and the storage of the energy obtained with a supercapacitor, converts the active tag into an autonomous device. These tags work in a low power mode in which they dynamically adjust their radio communication capabilities. Such a configuration depends on the application. We tested the tags in a real environment with testing parameters to check the modules, meaning more wake-ups over a longer time. Under these conditions, the tags gather enough energy to autonomously maintain standby operation on a sunny day for ten hours. In conclusion, this autonomous active tag is a demonstration that the integration of energy harvesting techniques, supercapacitor storage and the management of low power modes for transceivers, microcontrollers, and memories creates a device without energy dependencies that only depends on the requirements, and can be used in many applications related to, for instance, smart homes, smart cities, smart cars, and connected forests.

Brain Computer Interfaces are used to obtain relevant information from the electroencephalogram (EEG) with a concrete objective. The evoked potentials related to movement are much demanded nowadays, in particular the ones associated to imagery movement. The objective of this work is to develop simple algorithms to imagery motion detection that can be included in a non-invasive wearable that everybody can use in a comfortable way for new services and applications. A wearable implies low resources, which is the most important requirement that the algorithms have. A public database with 105 subjects doing an upper-limb imagery movement is used. We have developed two algorithms (FBA and BLA) based on three characteristics of the signal (correlation, wavelet energy per segment and wavelet energy per electrode). They are tested for different number of electrodes and frequency bands. The best performance is found for 6 electrodes. The beta band is not the only band who achieves good performances. In fact, in this study the range between 25 Hz - 30 Hz has obtained the best performance using 6 electrodes. The conclusions show that these simple algorithms not fit well with the wearable requirements. However, it shows the need of adaptive algorithms to bypass the differences between subjects. Also, it affirms that more electrodes not lead to a better information, as well as, less electrodes not lead to a worse information. The same goes for frequency, where not only the beta band have the information required that fits our needs.