988 and 0.963 respectively, and also have small errors (the root mean square errors are 3.64ms and 6.39ms respectively). Furthermore, the proposed system may be used to measure blood pressure indirectly based on ECG signal and BCG signal measured directly.Recent mobile and wearable electroencephalogram (EEG)-sensing technologies have been demonstrated to be effective for measuring rapid changes of spatio-spectral EEG correlates of brain and cognitive functions of interest with more ecologically natural settings. However, commercial EEG products are available commonly with a fixed headset in terms of the number of electrodes and their locations to the scalp practically constrains their generalizability for different demands of EEG and brain-computer interface (BCI) study. While most progress focused on innovation of sensing hardware and conductive electrodes, less effort has been done to renovate mechanical structures of an EEG headset. Recently, an electrode-holder assembly infrastructure was designed to be capable of unlimitedly (re)assembling a desired n-channel electrode headset through a set of primary elements (i.e., LEGO-like headset). The present work empirically demonstrated one of its advantage regarding coordinating the homogeneous or heterogeneous sensors covering the target regions of the brain. Towards this objective, an 8-channel LEGO headset was assembled to conduct a simultaneous event-related potential (ERP) recording of the wet- and dry-electrode EEG systems and testify their signal quality during standing still versus treadmill walking. The results showed that both systems returned a comparable P300 signal-to-noise ratio (SNR) for standing, yet the dry system was more susceptible to the movement artifacts during slow walking. The LEGO headset infrastructure facilitates a desired benchmark study, e.g., comparing the signal quality of different electrodes on non-stationary subjects conducted in this work, or a specific EEG and BCI application.The purpose of this study was to discriminate between left- and right-hand motor imagery tasks. We recorded the brain signals from two participants using a fNIRS system and compared different feature extraction (mean, peak, minimum, skewness and kurtosis) and classification techniques (linear (LDA) and quadratic discriminant analysis (QDA), support vector machine (SVM), logistic regression, K-nearest-neighbor (KNN), and neural networks with Levenberg-Marquardt (LMA), Bayesian Regularization (BRANN) and Scaled Conjugate Gradient (SCGA) training algorithms). The results showed poor classification accuracies (98%) when mean, peak and minimum were used as features.In general, the signal chain in modern mobile Brain-Computer Interfaces (BCIs) is subdivided into at least two blocks. These are usually wirelessly connected with digital signal processing part implemented separately and often stationary. BAY-1841788 This causes a limited mobility and results in an additional, although avoidable, latency due to the wireless transmission channel. Therefore, a novel, entirely mobile FPGA-based platform for BCIs has been designed and implemented. While featuring highly efficient adaptability to targeted algorithms due to the ultra low power Flash-based FPGA, the stackable system design and the configurable hardware ensure flexibility for the use in different application scenarios. Powered through a single Li-ion battery, the miniaturized system area of half the size of a credit card leads to high mobility and thus allow for real-world scenario applicability. A Bluetooth Low Energy extension can be connected without any significant area cost, if a wireless data or control signal transmission channel is required. The resulting system is capable of acquiring and fully processing of up to 32 EEG channels with 24 bit precision each and a sampling rate of 250-16k samples per second with a total weight less than 60 g.The millennial age group (18 to 30 years) spend at least 6 hours sitting, either in college or at their workspace. High screen time as a routine, is the major cause for numerous spinal problems. Despite the wide research carried out on postural abnormalities, there exists numerous unrequited queries with regards to lumbar lordosis estimations, due to indeterminate parameters such as age, gender, lifestyle and diet. This work emphasizes the proficient method by observing the posture of a person for early detection of obliteration in Lumbar Lordosis. This further contributes to efficient diagnosis and treatment of spine ailments. With a novel approach to hardware using the myRIO hardware coupled with LabVIEW for interactive interface, the calibration is enhanced using machine learning (ML) – kNN Classifier. The use of machine learning accounts for the variations in the ideal angles of segmented sagittal measures with respect to different subjects. The device is developed to be a non-invasive, user friendly instrument to analyse the casual seated posture trends of the subject. The male subjects are expected to show the tilt angles in the range of -16.3 to -17.2 degrees and similar threshold for females are -15.8 to -16.8 degrees. Out of 120 subjects taken into consideration, the device could accurately classify subjects with obliterated or normal lumbar lordosis). An accuracy and f1- score of 94% and 90% respectively was achieved by the ML model.The current work presents the development and technical validation, in terms of accuracy and latency, of a low-cost portable device that allows identifying possible risks of falling in people when they realize spinal trunk lateral movements. The device is comprised of an Inertial Measurement Unit (IMU) located on the lower back. Measurements are processed to get meaningful parameters such as rotation angles of the back when realizing lateral movements. In order to give performance feedback while doing the test, this device includes a Microcontroller as Raspberry Pi to return visual feedback to the person. The critical system feature is the latency of the system since getting the data of a movement until showing that on the feedback screen. For that reason, before to start assessing people, we propose a technical method using the Mikrolar Hexapod Robot R3000 for validating the system developed by simulating the movement of the back and recording it with a video camera to apply an offline Motion-to-Photon Latency analysis.