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Related papers: Spatial sampling of MEG and EEG revisited: From sp…

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The electroencephalography (EEG) signal is a non-stationary, stochastic, and highly non-linear bioelectric signal for which achieving high classification accuracy is challenging, especially when the number of subjects is limited. As…

Signal Processing · Electrical Eng. & Systems 2021-08-03 Xiangyun Li , Peng Chen , Zhanpeng Bao

Magnetoencephalography (MEG) is a noninvasive method for measuring magnetic flux signals caused by brain activity using sensor arrays located on or above the scalp. A common strategy for monitoring brain activity is to place sensors on a…

Medical Physics · Physics 2022-05-24 Wan-Jin Yeo , Samu Taulu , J. Nathan Kutz

Current non-invasive neuroimaging techniques trade off between spatial resolution and temporal resolution. While magnetoencephalography (MEG) can capture rapid neural dynamics and functional magnetic resonance imaging (fMRI) can spatially…

Neurons and Cognition · Quantitative Biology 2025-10-13 Beige Jerry Jin , Leila Wehbe

Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Estimating the location and magnitude of the current sources that generated…

Machine Learning · Statistics 2019-10-16 Hicham Janati , Thomas Bazeille , Bertrand Thirion , Marco Cuturi , Alexandre Gramfort

Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the…

Signal Processing · Electrical Eng. & Systems 2020-09-02 Ce Zhang , Azim Eskandarian

Magnetoencephalography (MEG) is an advanced imaging technique used to measure the magnetic fields outside the human head produced by the electrical activity inside the brain. Various source localization methods in MEG require the knowledge…

Methodology · Statistics 2017-07-06 Zhigang Yao , Ye Zhang , Zhidong Bai , William F. Eddy

Electroencephalography (EEG) and magnetoencephalography (MEG) play important and complementary roles in non-invasive brain-computer interface (BCI) decoding. However, compared to the low cost and portability of EEG, MEG is more expensive…

Signal Processing · Electrical Eng. & Systems 2026-02-10 Zhuo Li , Shuqiang Wang

MEG and EEG are noninvasive functional neuroimaging techniques that provide recordings of brain activity with high temporal resolution, and thus provide a unique window to study fast time-scale neural dynamics in humans. However, the…

Applications · Statistics 2015-11-13 Camilo Lamus , Matti S. Hamalainen , Emery N. Brown , Patrick L. Purdon

Brain computer interface (BCI) is the only way for some special patients to communicate with the outside world and provide a direct control channel between brain and the external devices. As a non-invasive interface, the scalp…

Quantitative Methods · Quantitative Biology 2018-08-15 Chuanqi Tan , Fuchun Sun , Wenchang Zhang , Shaobo Liu , Chunfang Liu

The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an…

Medical Physics · Physics 2016-10-07 Annalisa Pascarella , Francesca Pitolli

An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies have explored EEG-based classification of objects or concepts, often for passive viewing…

Machine Learning · Computer Science 2026-02-25 Anupam Sharma , Harish Katti , Prajwal Singh , Shanmuganathan Raman , Krishna Miyapuram

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer…

Signal Processing · Electrical Eng. & Systems 2024-12-25 Haili Ye , Stephan Goerttler , Fei He

We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned for single and multiple time point MEG data, and can estimate varying numbers…

Signal Processing · Electrical Eng. & Systems 2020-12-02 Dimitrios Pantazis , Amir Adler

The electroencephalogram (EEG) is a powerful method to understand how the brain processes speech. Linear models have recently been replaced for this purpose with deep neural networks and yield promising results. In related EEG…

Audio and Speech Processing · Electrical Eng. & Systems 2022-07-25 Lies Bollens , Tom Francart , Hugo Van Hamme

Detecting where and when brain regions activate in a cognitive task or in a given clinical condition is the promise of non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG). This problem, referred to as…

Machine Learning · Statistics 2020-11-26 Jérôme-Alexis Chevalier , Alexandre Gramfort , Joseph Salmon , Bertrand Thirion

In this paper, we propose a novel source model for a magnetoencephalography (MEG) inverse problem that combines a conventional extended parametric approach and an imaging approach.Our aim is to separately identify a focal current source and…

Signal Processing · Electrical Eng. & Systems 2023-02-27 Takaaki Nara , Ten-yu Yang , Kenta Kabashima

High temporal resolution measurements of human brain activity can be performed by recording the electric potentials on the scalp surface (electroencephalography, EEG), or by recording the magnetic fields near the surface of the head…

Data Analysis, Statistics and Probability · Physics 2015-01-22 Kevin H. Knuth

Magnetoencephalography (MEG) enables the study of brain activity by recording the magnetic fields generated by neural currents and has become an important technique for neuroscientists in research and clinical settings. Unlike the…

Magnetoencephalography (MEG) conventionally operates within high-performance magnetic shields due to the extremely weak magnetic field signals from the measured objects and the narrow dynamic range of the magnetic sensors employed for…

Medical Physics · Physics 2024-03-20 Yosuke Ito , Hiroyuki Ueda , Takenori Oida , Takahiro Moriya , Akinori Saito , Motohiro Suyama

Electroencephalogram (EEG) has been a core tool used in functional neuroimaging in humans for nearly a hundred years. Because it is inexpensive, easy to implement, and noninvasive, it also represents an excellent candidate modality for use…

Neurons and Cognition · Quantitative Biology 2021-11-18 PK Douglas , DB Douglas