Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.Comment: 10 page

A cost effective approach for the practical realisation of a demonstration platform for brain machine interface

Over the last two decades, human brain functions have attracted a significant attention among researchers across a broad engineering spectrum. The most important field among the others, is Brain Computer Interface (BCI) which is a direct functional interaction between a human brain and external devices. In the past, the set-up for BCI research is costly and complex. In this paper, a cost effective way of implementing and designing a demonstration platform for BCI research is presented, featuring a low-cost hardware implementation based on an open-source electronics platform Arduino® with the view of being compatible with MATLAB® and Simulink®, and a commercial non-invasive electroencephalogram (EEG) recording device, Emotive®. Due to the compatibility with MATLAB® and Simulink®, and the chosen EEG logging device, the developed hardware and software platform can work seamlessly with several widely accepted BCI and EEG signal processing open-source software within the BCI research community, such as EEGLAB and OpenViBE. With the two-way communication and hardware-in-the-loop concept embedded within the design process, the developed platform can be tuned in an online fashion, which bears the long-term objective of investigating a holistic human-in-the-loop feedback control mechanism so that human and machines can collaborate in a more intelligent and natural way. The presented approach can be beneficial for BCI practitioners to set up their first inexpensive test rig and carry out fast prototyping in related activities

Discrete classification technique applied to TV advertisements liking recognition system based on low‑cost EEG headsets

Background: In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. Methods: By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. Results: The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. Conclusions: This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.Ministerio de Economía y Competitividad HERMES TIN2013-46801-C4-1-rJunta de Andalucia Simon TIC-805

Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies

The recent boom in the Internet of Things (IoT) will turn Smart Cities and Smart Homes (SH) from hype to reality. SH is the major building block for Smart Cities and have long been a dream for decades, hobbyists in the late 1970s made Home Automation (HA) possible when personal computers started invading home spaces. While SH can share most of the IoT technologies, there are unique characteristics that make SH special. From the result of a recent research survey on SH and IoT technologies, this paper defines the major requirements for building SH. Seven unique requirement recommendations are defined and classified according to the specific quality of the SH building blocks

Neural Prosthetic Advancement: identification of circuitry in the Posterior Parietal Cortex

There are limited options for rehabilitation following an established Spinal Cord Injury (SCI) resulting in paralysis. For most of the individuals affected, SCI means a lifetime of confinement to a wheelchair and overall reduced independence. Brain-Computer and Brain-Machine Interface (BCI and BMI) techniques may be of aid when used for assistive purposes. However, these techniques are still far from being implemented in daily rehabilitative practice. Existing literature on the use of BCI and BMI techniques in SCI is limited and focuses on the extraction of motor control signals from the primary motor cortex (M1). However, evidence suggests that in long-term established SCI the functional activation of motor and premotor areas tends to decrease over time. In the present project, we explore the possibility of successful implementation of assistive BCI and BMI systems using posterior parietal areas as extraction sites of motor control activity. Firstly, we will investigate the representation of space in the posterior parietal cortex (PPC) and whether evidence of body-centered reference frames can be found in healthy individuals. We will then proceed to extract information regarding the residual level of motor imagery activity in individuals suffering from long-term and high-level SCI. Our aim is to ascertain whether functional activation of motor and posterior areas is comparable to that of matched controls. Finally, we will present work that was done in collaboration with the Netherlands Organisation for Applied Scientific Research that can offer an example of successful application of a BCI technique for rehabilitation purposes

Diseño y simulación de un filtro digital para Señales EEG con el paradigma de Imaginación Motora en FPGA

En la actualidad, los filtros digitales tienen diversas aplicaciones en distintas ramas de ingeniería como Biomédica, Electrónica, Telecomunicaciones. En diversos centros de investigación relacionados a señales Electroencefalográficas (EGG) presentan ciertas limitaciones en la etapa de pre-procesamiento de las señales electroencefalográficas debido al alto costo de equipos que permiten realizar la etapa de pre – procesamiento, adquisición y visualización de la señal EEG filtrada. El objetivo del presente trabajo es diseñar y simular un filtro digital en hardware para señales EEG del paradigma de imaginación motora en un sistema embebido FPGA (Field Programmable Gate Array), cuyo propósito es poder desarrollar la etapa de pre-procesamiento de la señal EEG del paradigma de imaginación motora. Utilizar los recursos de hardware y software del FPGA permite tener una amplia flexibilidad en poder diseñar un filtro digital. Las herramientas de simulación Matlab, Modelsim, permiten una solución practica para la visualización de la señal EEG filtrada. Se opta por el uso del filtro FIR basado en el método de ventana de Hamming y Blackman. Adicionalmente, se trabajó con una base de datos EEG de 60 canales el cual se utiliza los canales C3 y C4 con la finalidad de trabajar con las ondas electroencefalográficas de imaginación motora Beta y Mu. En el diseño del filtro digital en software se emplea el lenguaje de programación VHDL, asimismo en los resultados obtenidos se realiza una comparación del filtro digital FIR usando el método de ventana de Hamming y Blackman que mediante las pruebas realizadas en software se demuestra que el método de ventana Blackman presenta una mejor respuesta de la señal Electroencefalográfica para la etapa de pre -procesamiento ,brindando una herramienta en software que permitirá a los usuarios realizar diferentes estudios complementarios al área de pre – procesamiento de señales EEG.Nowadays, the digital filters have different applications in different branches of engineering such as Biomedical, Electronics, Telecommunications. In various research centers related to Electroencephalographic (EGG) signals, they present certain limitations in the preprocessing stage of the electroencephalographic signals due to the high cost of equipment that allows the pre-processing, acquisition and visualization of the filtered EEG signal to be carried out. . The objective of this work is to design and simulate a hardware digital filter for EEG signals of the motor imagination paradigm in an embedded FPGA system (Field Programmable Gate Array), whose purpose is to develop the pre-processing stage of the EEG signal of the motor imagination paradigm. Using the hardware and software resources of the FPGA allows a wide flexibility in being able to design a digital filter. The Matlab simulation tools, Modelsim, allow a practical solution for the visualization of the filtered EEG signal. The use of the FIR filter based on the Hamming and Blackman window method is chosen. Additionally, we worked with a 60-channel EEG database which uses channels C3 and C4 in order to work with the EEG waves of motor imagination Beta and Mu. The design of the digital filter in software uses the language of VHDL programming, also in the results obtained a comparison of the digital FIR filter is made using the Hamming and Blackman window method, which through tests carried out in software shows that the Blackman window method presents a better response of the Electroencephalographic signal for the pre-processing stage, providing a software tool that will allow users to carry out different complementary studies to the pre-processing area of EEG signals.Campus Lima Centr

Novel Transfer Learning Approaches forImproving Brain Computer Interfaces

Despite several recent advances, most of the electroencephalogram(EEG)-based brain-computer interface (BCI) applications are still limited to the laboratory due to their long calibration time. Due toconsiderable inter-subject/inter-session and intra-session variations, atime-consuming and fatiguing calibration phase is typically conductedat the beginning of each new session to acquire sufficient labelled train-ing data to train the subject-specific BCI model.This thesis focuses on developing reliable machine learning algorithmsand approaches that reduce BCI calibration time while keeping accu-racy in an acceptable range. Calibration time could be reduced viatransfer learning approaches where data from other sessions or sub-jects are mined and used to compensate for the lack of labelled datafrom the current user or session. In BCI, transfer learning can beapplied on either raw EEG, feature or classification domains.In this thesis, firstly, a novel weighted transfer learning approach isproposed in the classification domain to improve the MI-based BCIperformance when only few subject-specific trials are available fortraining.Transfer learning techniques should be applied in a different domainbefore the classification domain to improve the classification accuracyfor subjects whom their subject-specific features for different classesare not separable. Thus, secondly, this thesis proposes a novel regu-larized common spatial patterns framework based on dynamic timewarping and transfer learning (DTW-R-CSP) in raw EEG and featuredomains.In previous transfer learning approaches, it is hypothesised that thereare enough labelled trials available from the previous subjects or ses-sions. However, in the case when there are no labelled trials available from other subjects or sessions, domain adaptation transfer learningcould potentially mitigate problems of having small training size byreducing variations between the testing and training trials. Thus, todeal with non-stationarity between training and testing trials, a novelensemble adaptation framework with temporal alignment is proposed

Development of Brain Machine Interface Systems and its Applications to Prosthetic Hand Control

東京電機大学201