AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements

Translation, Cross-Cultural Adaptation, and Validation of the Foot Posture Index (FPI-6)-Italian Version

Since foot posture is one of the main predictors of lower limb musculoskeletal injuries, it is crucial to use appropriate tools to define the foot's posture. The Foot Posture Index is, therefore, a reliable method to measure foot posture and is widely known and used in clinics and research. This study aimed to translate the Foot Posture Index 6 (FPI6) into Italian and to assess its psychometric properties. Translation and cross-cultural adaptation were obtained using a popular guideline. Two examinators assessed 68 subjects, and data were collected to test intra/inter-rater reliability, internal consistency and cross-cultural validity. The Italian version of FPI6 showed excellent inter- and intra-rater reliability (ICC 0.96 and 0.97), and Cronbach's alpha coefficient was 0.9, thus showing excellent internal consistency. The FPI-6 version has proved to be reliable in terms of inter- and intra-rater reliability and can, therefore, be used in clinical practice and scientific research.

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems.

Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth

An end-fire radiating implantable antenna with a small footprint and broadband operation at the frequency range of 3–5 GHz is proposed for high-data-rate wireless communication in a brain–machine interface. The proposed Vivaldi antenna was implanted vertically along the height of the skull to avoid deformation in the radiation pattern and to compensate for a gain–loss caused by surrounding lossy brain tissues. It was shown that the vertically implanted end-fire antenna had a 3 dB higher antenna gain than a horizontally implanted broadside radiating antenna discussed in recent literature. Additionally, comb-shaped slot arrays imprinted on the Vivaldi antenna lowered the resonant frequency by approximately 2 GHz and improved the antenna gain by more than 2 dB compared to an ordinary Vivaldi antenna. An antenna prototype was fabricated and then tested for verification inside a seven-layered semi-solid brain phantom where each layer had similar electromagnetic material properties as actual brain tissues. The measured data showed that the antenna radiated toward the end-fire direction with an average gain of −15.7 dBi under the frequency of interest, 3–5 GHz. A link budget analysis shows that reliable wireless communication can be achieved over a distance of 10.8 cm despite the electromagnetically harsh environment.