Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

From Wiley via Jisc Publications RouterHistory: received 2021-01-22, rev-recd 2021-05-14, accepted 2021-05-28, pub-electronic 2021-06-24Article version: VoRPublication status: PublishedFunder: Engineering and Physical Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/S020179/1, EP/P02713X/1Abstract: This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind‐source separation methods, and helps enable in the wild EEG recordings to be performed

Passivity Based Control Techniques

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2005Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2005Bu çalışmada pasif sistemlerin kontrolü için geliştirilen teknikler tanıtılmaya çalışılmıştır. İlgilenilen sistemler Euler-Lagrange sistemleri ile sınırlı tutulmuştur. Önce Klasik Mekaniğe dair temel kavramlar açıklanmaya çalışılmış daha sonra Euler-Lagrange denklemlerinin hem Newton’un hareket yasalarından hem de Hamilton İlkesinden çıkartılması ve verilen bir sistemin Euler-Lagrange modelinin çıkartılması gösterilmeye çalışılmıştır. Daha sonra Euler-Lagrange modeli verilen bir sistemin kontrolünde kullanılan Pasifliğe Dayalı Kontrol tekniğinin teorik alt yapısının açıklanması için gerekli temel kavramlar ve teoremler açıklanmaya çalışılmıştır. Son olarak anlatılan tekniklerin birkaç uygulaması verilmiş ve simülasyonları yapılmıştır.In this study the techniques which have been developed for control of passive systems are presented. The systems, which are in interest, are Euler-Lagrange systems. First of all some fundamental properties of Classical Mechanics are presented. Then the Euler-Lagrange equations are derived from both Newton s Laws and Hamilton s principle. And derivation of the Euler-Lagrange model of a given system is also presented. Then, following this, the theoretical concepts of the passivity based control are presented. At the end the techniques are applied to some mechanical systems. The simulation results are also included.Yüksek LisansM.Sc

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind‐source separation methods, and helps enable in the wild EEG recordings to be performed

Implementation of a batch normalized deep LSTM recurrent network on a smartphone for human activity recognition

In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network (RNN) model for the classification of human daily life activities by using the accelerometer and gyroscope data of a smartphone. The proposed model was trained by using the open-source TensorFlow library, optimised and deployed on an Android smartphone. Hardware resource requirements for the implementation are empirically investigated and the effect of data quantization on the accuracy of the implementation is discussed. In addition, we profile the power budget for running the proposed model on smartphone. Results of this work will be of use for deep learning implemented on edge computing devices, which leverages the user privacy as the raw data never leaves the person