实时解码fNIRS脑机接口：自适应高斯混合模型与卡尔曼滤波器结合

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"这篇研究论文探讨了如何使用自适应高斯混合模型分类器和卡尔曼估计器对基于功能近红外光谱(fNIRS)的脑计算机接口进行实时解码，以提高解码精度。该框架结合了通用线性模型(GLM)和卡尔曼滤波器，通过模拟实验结果显示，GMMAC在二分类任务中的性能显著优于线性判别分析(LDA)和支持向量机(SVM)。" 本文主要涉及以下知识点： 1. **功能近红外光谱(fNIRS)**：fNIRS是一种非侵入性的神经成像技术，能够测量大脑皮层的血流量和氧合水平，进而推断大脑活动。它利用近红外光穿透头皮和颅骨，检测到血液中氧合血红蛋白和脱氧血红蛋白的变化，与大脑的神经活动相关联。 2. **脑计算机接口(BCI)**：BCI是一种允许人与机器直接交互的技术，无需通过传统的外围设备（如键盘、鼠标）。BCI系统通常基于大脑活动的信号，如fNIRS、EEG或fMRI等，将这些信号转化为控制指令。 3. **自适应高斯混合模型(GMMAC)**：GMM是一种概率模型，常用于模式识别和数据建模。在BCI中，GMMAC是未监督学习方法，它可以自动发现数据的潜在分布，不断调整模型参数以优化分类性能。在本文中，GMMAC在实时解码中表现出色，提高了分类准确率。 4. **通用线性模型(GLM)**：GLM是一种统计模型，广泛应用于各种领域，包括神经科学。在fNIRS数据处理中，GLM可以用来描述大脑活动与刺激之间的关系，通过建模血流动力学响应来分析信号。 5. **卡尔曼滤波器(Kalman Estimator)**：卡尔曼滤波是一种递归的估计算法，用于处理随机过程中的不确定性和噪声。在BCI中，卡尔曼滤波器可以用来平滑和优化信号，提高解码的稳定性。 6. **实时解码**：实时解码是BCI系统的一个关键特性，它要求系统能够迅速、准确地解析大脑信号并即时反馈。本文提出的框架旨在实现这种实时性能，以提升用户体验和交互效率。 7. **模拟实验结果比较**：实验对比了GMMAC、LDA和SVM三种分类器的性能。结果显示，GMMAC在二分类任务中的准确率显著高于LDA和SVM，这表明在fNIRS-BCI应用中，自适应GMM分类器配合卡尔曼滤波器的策略可能更为有效。 8. **分类器性能评估**：评估指标通常包括准确率、精确率、召回率、F1分数等。本文中未具体提供这些指标，但91.9%的分类准确率表明了GMMAC在改善解码性能上的优势。该研究通过创新性地结合自适应高斯混合模型和卡尔曼滤波器，为fNIRS-BCI系统的实时解码提供了新的思路，为未来开发更高效、准确的BCI系统奠定了基础。

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Real-time decoding for fNIRS-based Brain Computer

Interface using adaptive Gaussian mixture model

classifier and Kalman estimator

Yao Zhang

College of Precision Instrument and

Optoelectronics Engineering

Tianjin University

Tianjin, China

viyoo_zhang@tju.edu.cn

Bingyuan Wang

College of Precision Instrument and

Optoelectronics Engineering

Tianjin University

Tianjin, China

wangbingyuan@tju.edu.cn

Feng Gao

College of Precision Instrument and

Optoelectronics Engineering

Tianjin Key Laboratory of Biomedical

Detecting Techniques and Instruments

Tianjin University

Tianjin, China

gaofeng@tju.edu.cn

Abstract— In this paper, we investigate a real-time analysis of

NIRS data by using an unsupervised Gaussian mixture model

adaptive classifier (GMMAC) for a framework consisting of the

general linear model (GLM) and the Kalman estimator to

improve decoding accuracy. Simulation experiment results

demonstrate that the GMMAC (91.9%) perform significantly

better than linear discriminant analysis (LDA, 51.9%) and

support vector machines (SVM, 51.1%) classifiers in binary

problems.

Keywords—Functional near-infrared spectroscopy, Brain-

computer interface, adaptive real-time decoding, General linear

model, Kalman estimator, Gaussian mixture model, variational

inference

I. I

NTRODUCTION

Functional near-infrared spectroscopy (fNIRS) is the

relatively new non-invasive neuroimaging modality for brain-

computer interface (BCI) to measure the changes of cerebral

blood oxygenation associated with brain activities [1, 2]. These

changes are caused by the concentration variation of oxy-

hemoglobin (HbO) and deoxy-hemoglobin (HbR), which are

primary absorbing chromophores in the capillaries of the brain

[1, 3]. Since fNIRS is less susceptible to environmental noises

and electrophysiological artifacts compared to EEG [3], it is

believed that fNIRS-BCI is ideally suited for the development

of portable and potentially clinical real-time system for future

applications.

Over the past few years, several research groups attempt to

develop real-time data analysis schemes to decoding different

experimental paradigms and assess brain activation at the same

time as data collection, and combination of more powerful and

faster computers [4]. At the same time, real-time fNIRS data

analysis can use biofeedback to develop experimental designs

for subjects. Therefore, an adaptive real-time high-precision

decoding method is proposed and applied to clinical

neurofeedback and neural rehabilitation training.

In our current paper, we have taken advantages of a real-

time NIRS data analysis framework combined with the GLM

and the Kalman estimator. The GLM has been established as a

standard method for fMRI data analysis, and has also been

applied to fNIRS studies using block and event-related designs

[5]. In group analysis, the GLM is used to produce a subject-

specific contrast relationship from X-weight in regression to the

hemodynamic response function (HRF). In our model, we used

the Kalman estimator to estimate the X coefficients recursively

[6]. Classification techniques are used to identify the different

brain signals that are generated by the subjects. In this article,

we present the GMMAC for adaptive classification of fNIRS

data. Then we use simulated experiments to test this method.

These simulations contained in fNIRS neural activation

patterns changes, including shift of activation region,

contraction and expansions of activation region, during a two-

class decoding task. Our results show that the GMMAC applied

to real-time decoding can well track the activation region

changes, and can get high-decoding accuracy.

II. M

ETHODS

A. General linear model

Time series of HbO and HbR signal data were analyzed

using a GLM [4], the model can be expressed as follows:

Δ= +H

, (1)

where the

Δ∈

represents the changes of hemodynamic

variable at the N points time series in D channels. There

∈H

(where L is the number of explanatory variables) is

known as the design matrix including a set of explanatory

variables and models the observed NIRS time series. In our job,

the design matrix H contains five explanatory variables

representing canonical HRF (cHRF), heartbeat signal, Mayer

wave signal, breathing signal, and constant respectively. The

∈

is the regression coefficients quantifying the

magnitude of the explanatory variables. The final term

is the

residual error obeying independent zero-mean Gaussian

distribution. We used the Kalman estimator to solve the (1) for

real-time estimation of each point time.

Kalman estimator

The Kalman filter method is an adaptive tracking scheme

that perform an optimal estimation of the state of a process

using a recursively regularized linear inversion routine [4]. In

this study, the Kalman filter was used as the GLM coefficients

2018 Asia Communications and Photonics Conference (ACP)

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实时解码fNIRS脑机接口：自适应高斯混合模型与卡尔曼滤波器结合

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