scholarly journals Time-Series Prediction of the Oscillatory Phase of EEG Signals Using the Least Mean Square Algorithm-Based AR Model

2020 ◽  
Vol 10 (10) ◽  
pp. 3616 ◽  
Author(s):  
Aqsa Shakeel ◽  
Toshihisa Tanaka ◽  
Keiichi Kitajo
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Computational Cost ◽  
Ar Model ◽  
Mean Square ◽  
Least Mean Square ◽  
Direct Role ◽  
Alpha Oscillations ◽  
Oscillatory Phase

Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cortical activities. However, recent studies on alpha oscillations (particularly alpha phase) hypothesize that they have an active and direct role in the mechanisms of attention and working memory. To understand the role of alpha oscillations in several cognitive processes, accurate estimations of phase, amplitude, and frequency are required. Herein, we propose an approach for time-series forward prediction by comparing an autoregressive (AR) model and an adaptive method (least mean square (LMS)-based AR model). This study tested both methods for two prediction lengths of data. Our results indicate that for shorter data segments (prediction of 128 ms), the AR model outperforms the LMS-based AR model, while for longer prediction lengths (256 ms), the LMS- based AR model surpasses the AR model. LMS with low computational cost can aid in electroencephalography (EEG) phase prediction (alpha oscillations) in basic research to reveal the functional role of the oscillatory phase as well as for applications for brain-computer interfaces.

scholarly journals Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model

2021 ◽  
Vol 11 (1) ◽  
pp. 38
Author(s):  
Aqsa Shakeel ◽  
Takayuki Onojima ◽  
Toshihisa Tanaka ◽  
Keiichi Kitajo
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Loop System ◽  
Ar Model ◽  
Brain State ◽  
Mean Square ◽  
Least Mean Square ◽  
Closed Loop System ◽  
Alpha Oscillations ◽  
State Dependent

It is a technically challenging problem to assess the instantaneous brain state using electroencephalography (EEG) in a real-time closed-loop setup because the prediction of future signals is required to define the current state, such as the instantaneous phase and amplitude. To accomplish this in real-time, a conventional Yule–Walker (YW)-based autoregressive (AR) model has been used. However, the brain state-dependent real-time implementation of a closed-loop system employing an adaptive method has not yet been explored. Our primary purpose was to investigate whether time-series forward prediction using an adaptive least mean square (LMS)-based AR model would be implementable in a real-time closed-loop system or not. EEG state-dependent triggers synchronized with the EEG peaks and troughs of alpha oscillations in both an open-eyes resting state and a visual task. For the resting and visual conditions, statistical results showed that the proposed method succeeded in giving triggers at a specific phase of EEG oscillations for all participants. These individual results showed that the LMS-based AR model was successfully implemented in a real-time closed-loop system targeting specific phases of alpha oscillations and can be used as an adaptive alternative to the conventional and machine-learning approaches with a low computational load.

Online Learning for Time Series Prediction of AR Model with Missing Data

2019 ◽  
Vol 50 (3) ◽  
pp. 2247-2263 ◽  
Author(s):  
Haimin Yang ◽  
Zhisong Pan ◽  
Qing Tao
Keyword(s):  
Time Series ◽  
Online Learning ◽  
Missing Data ◽  
Time Series Prediction ◽  
Ar Model

scholarly journals Interval Arithmetic based Adaptive Filtering Technique for Removal of Noise in Audio Signal

2020 ◽  
Vol 9 (1) ◽  
pp. 1900-1905
Keyword(s):  
Interval Analysis ◽  
Adaptive Filters ◽  
Optimization Problem ◽  
Computational Cost ◽  
Audio Signal ◽  
Interval Data ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Interval Newton Method

Active noise cancellation is one of the fundamental problems in acoustic signal processing. The proposed work focuses on the enhancement of audio signal quality by cancelling the noise using interval analysis (arithmetic). An adaptive filters basically works on the concept of optimal weight calculations which is an optimization problem. This optimization problem can be more effectively solved using interval analysis. Interval analysis gives the boundary of the weight co officiants. Using interval Newton method, the weight co officiants are found. This algorithm is tested for noise cancellation of speech signal. The three adaptive filters algorithm used for comparison with the obtained results are Least Mean Square (LMS), Recursive Mean Square (RMS) filters and Kernel based filters. It is observed that the parameters mean square error is very less. The speed of convergence and signal to noise ratio is improved as compared to kernel methods. But processing time is very high and computational cost is doubled, as interval data includes infimum and supremum values. This algorithm can be used in noise cancelling headphones.

scholarly journals Multichannel Active Noise Control Based on Filtered-x Affine Projection-Like and LMS Algorithms with Switching Filter Selection

2019 ◽  
Vol 9 (21) ◽  
pp. 4669 ◽  
Author(s):  
Ángel A. Vázquez ◽  
Eduardo Pichardo ◽  
Juan G. Avalos ◽  
Giovanny Sánchez ◽  
Hugo M. Martínez ◽  
...  
Keyword(s):  
Steady State ◽  
Computational Cost ◽  
Lms Algorithm ◽  
Alternative Methods ◽  
Mean Square ◽  
Least Mean Square ◽  
Affine Projection ◽  
Mean Square Errors ◽  
Switching Filter ◽  
Filter Selection

Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.

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