Scalp EEG Acquisition in a Low-Noise Environment: A Quantitative Assessment

Zandi, Ali Shahidi; Dumont, Guy A.; Yedlin, M.; Lapeyrie, Philippe; Sudre, C.; Gaffet, Stéphane

doi:10.1109/tbme.2011.2158647

Cited by 15 publications

(11 citation statements)

References 43 publications

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“…Although having slightly higher values in LSBB, backward counting-rest ratios are mainly distributed around one in both environments, suggesting that gamma band may not be significantly involved in continuous counting tasks. We have found this to be the case also in a previous study [6]. On the other hand, matching-rest ratios are mainly greater than one in both environments, and their values are higher in LSBB at temporal and occipital regions of the brain.…”

Section: Discussionsupporting

confidence: 88%

“…In a previous study, we have shown that EEG signals acquired in LSBB are free from electromagnetic interference and can be acquired and analyzed without the need for notch filtering with battery-powered EEG equipment [6,7]. This facilitates examination of gamma-band EEG signals acquired in the LSBB while searching for functionally significant high-frequency EEG correlates which would otherwise be obscured by common filters of high-frequency electromagnetic noise.…”

Section: Introductionmentioning

confidence: 98%

“…To reduce the disruptive effects of electrical interference and movement (i.e. noise), depending on the neuronal activity of interest, EEG is often band-pass filtered at different frequencies from 0.5 to 100 Hz and is then studied in terms of activity across different frequency bands; including (0-4 Hz), (4)(5)(6)(7), (8)(9)(10)(11)(12) Hz), (12-30 Hz), and (30-100 Hz). Clinically, EEG frequencies are most often studied from 0.5 to 30 Hz with the gamma ( ) band being ignored.…”

Section: Introductionmentioning

confidence: 99%

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Ultra-low Noise EEG at LSBB: New results

et al. 2016

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Abstract. In this study, we investigate functional correlates of gamma band oscillations in low-noise EEG signals acquired in the LSBB shielded capsule and compare them to signals acquired in a typical hospital environment. Using a research-grade EEG acquisition system, we acquired 64-channel EEG recordings from three volunteers performing several cognitive, sensory, and motor tasks in both LSBB and hospital settings. Time-frequency analysis on the signals acquired in both environments reveals that the task-induced increase in gamma band (>30 Hz) energy relative to the resting state EEG is more prominent in signals acquired at LSBB, suggesting that task-specific changes in EEG are better reflected and more readily detected in signals acquired at LSBB. These results further demonstrate the potential value of low-noise settings such as the LSBB for conducting challenging highfrequency EEG studies.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Ultra-low Noise EEG at LSBB: New results

et al. 2016

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“…In two previous studies [1], [2] we have demonstrated the potential of the ultra-low noise shielded capsule at the heart of LSBB is an ideal environment for acquiring ultra-high, wideband electroencephalographic (EEG) signals without the need of filtering. The EEG has proven to be a useful information source in the analysis of brain activity, neural plasticity, and the diagnosis of various neurological disorders, as well as the development of intelligent brain-computer interfaces (BCIs).…”

Section: Introductionmentioning

confidence: 99%

Ultra-low Noise EEG at LSBB: Effective Connectivity Analysis

Hamzei¹,

Steeves²,

Kramer³

et al. 2019

E3S Web Conf.

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In this study, we further investigate electroencephalographic (EEG) data recorded during October 2014 in the ultra-shielded capsule at LSBB, with a focus on the study of task-specific Granger-causal effective connectivity pat-terns. In previous studies, we showed that noise-free EEG signals acquired in LSBB are suitable for analysis of activity patterns in high frequency bands, i.e. 30 Hz and above. We previously demonstrated that increases in task/rest gamma band (30-70 Hz) energy ratios during ankle and wrist movements are more prominent in the LSBB capsule than in an above-ground hospital environ-ment. The present study extends previous analyses by examining gamma-band connectivity, i.e. the functional patterns of interaction between 64 channels of EEG within the gamma band during motor tasks. We use parameters from a MultiVariate Auto-Regressive (MVAR) model to estimate effective connectivity in 10-second batches of EEG and report the average patterns across all batches in which subjects repetitively move their ankle/wrist. We report the gamma-band connectivity results in a reduced form as strength of hemispheric and inter-regional connections. The analysis reveals that for some subjects, significant channel-wise connections in the LSBB capsule outnumber those in the hospital, suggesting that patterns of gamma-band connectivity are better reflected in low-noise environments. This study again demonstrates the poten-tial of the ultra-shielded capsule and motivates further protocol enhancements and analysis methods for conducting future high-frequency EEG studies within LSBB.

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“…[10][11][12][13][14][15][16][17][18][19][20][21][22] Complexity reduction and fast convergence of the noise cancelation algorithm have remained a topic of intense research. This is because of the fact that with increase in the data transmission rate increases the order of filter.…”

Section: Introductionmentioning

confidence: 99%

Efficient block processing of long duration biotelemetric brain data for health care monitoring

Soumya

Rahman

Reddy

et al. 2015

Review of Scientific Instruments

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In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS, which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence.

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Scalp EEG Acquisition in a Low-Noise Environment: A Quantitative Assessment

Cited by 15 publications

References 43 publications

Ultra-low Noise EEG at LSBB: New results

Ultra-low Noise EEG at LSBB: New results

Ultra-low Noise EEG at LSBB: Effective Connectivity Analysis

Efficient block processing of long duration biotelemetric brain data for health care monitoring

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