Elsevier

Neuroscience Letters

Volume 561, 21 February 2014, Pages 166-170
Neuroscience Letters

About the cortical origin of the low-delta and high-gamma rhythms observed in EEG signals during treadmill walking

https://doi.org/10.1016/j.neulet.2013.12.059Get rights and content

Highlights

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    Spectral and time–frequency EEG analysis was performed in ambulatory context.

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    Motion artifacts may affect EEG signal integrity up to 15 Hz.

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    EEG and accelerometer signals exhibit similar time–frequency properties.

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    Cortical origin of low-delta and high-gamma bands during locomotion is put in doubt.

Abstract

This paper presents a spectral and time–frequency analysis of EEG signals recorded on seven healthy subjects walking on a treadmill at three different speeds. An accelerometer was placed on the head of the subjects in order to record the shocks undergone by the EEG electrodes during walking. Our results indicate that up to 15 harmonics of the fundamental stepping frequency may pollute EEG signals, depending on the walking speed and also on the electrode location. This finding may call into question some conclusions drawn in previous EEG studies where low-delta band (especially around 1 Hz, the fundamental stepping frequency) had been announced as being the seat of angular and linear kinematics control of the lower limbs during walk. Additionally, our analysis reveals that EEG and accelerometer signals exhibit similar time–frequency properties, especially in frequency bands extending up to 150 Hz, suggesting that previous conclusions claiming the activation of high-gamma rhythms during walking may have been drawn on the basis of insufficiently cleaned EEG signals. Our results are put in perspective with recent EEG studies related to locomotion and extensively discussed in particular by focusing on the low-delta and high-gamma bands.

Introduction

Recently, numerous experimental results have indicated a strong involvement of the brain during locomotion. Significant changes in motor and cognitive demands (i.e. spatial attention) have been observed in the context of bipedal walking in unknown or cluttered dynamic environments [8], [12], [21], [25]. Functional neuroimaging studies have shown that the primary motor cortex is recruited during rhythmic foot or leg movements [9], [11], [16], [17], [19], [26]. Additionally, the technique of functional near-infrared spectroscopy (fNIRS) has allowed to detect involvement of frontal, premotor and supplementary motor areas during walking [15], [28].

All those results were obtained using imagery techniques which are characterized by a good spatial but poor temporal resolution. In contrast, electroencephalography (EEG) is a measurement technique offering a sufficiently good temporal resolution to study the dynamics of brain. However, EEG study of cortical activity elicited during walk is highly challenging: EEG signals are by essence noisy and may be affected by different artifacts generated either by extracerebral physiological activity or by the gait itself [7].

Two strategies have thus been developed in the literature in order to overcome these experimental difficulties. The static approach consists in focusing on simplified foot or leg movements which imply common cerebral processes with gait. In these experimental protocols, subjects are mainly static and produce only limited lower limb movements. On the other hand, the dynamic approach consists in recording EEG signals from subjects walking on a treadmill. In this case, a powerful analysis technique to discriminate the different artifact contributions from the real cortical signals is of course required. Regrettably, the results of those different analyses are most of the time partially, if not totally, incompatible regarding both the location of the brain areas activated and the frequency bands of interest [5], [6].

In this paper, the EEG signals recorded during treadmill walking are analyzed and compared with data acquired by an accelerometer placed on the head of each subject. Similarities between both types of signals are presented and extensively discussed in order to bring new clues in the general understanding of EEG signals recorded during human locomotion, in particular for the very low and very high frequency bands.

Section snippets

Data collection

Seven healthy volunteers (5 males and 2 females) without any known physical or neurological disorders participated in this experiment (age-range: 25–33 years) whose protocol was extensively described elsewhere [7]. Basically, one of the objectives of this data collection was to assess the feasibility of developing a brain–computer interface under ambulatory conditions. Therefore, each subject walked bare feet on a treadmill at 1.5, 3 and 4.5 km/h wearing an EEG cap (32 passive electrodes)

Results

Common harmonics were found in the spectra of EEG electrodes and the accelerometer. These harmonics correspond to the fundamental stepping frequency of the subjects, which ranges from about 0.6 Hz at 1.5 km/h to 1 Hz roughly at 4.5 km/h. Box-plots shown in Fig. 1 clearly indicate that the number of harmonics present in the spectra is monotonously increasing with the walking speed. Here, harmonics with Signal to Noise Ratio >2 are considered, the signal being the peak amplitude at frequency f

Discussion

In addition to “traditional” EEG artifacts (ocular, muscular, power line, …), EEG recordings realized in ambulatory conditions are degraded by specific sources of noise [6], [7]. Triboelectric noise is generated by movement, friction and flexion of the cable components, resulting in a static or piezoelectric movement transducer effect [30]. Electrode movements are produced by movements of the head, but also by the shocks undergone by the whole body at each step, which—albeit significantly

Conclusions

Despite the inherent difficulties arising when analyzing EEG signals under ambulatory conditions, several groups have recently published papers about EEG decoding of movements or the fundamental analysis of mechanisms taking place in the brain during locomotion. By simultaneously recording the data coming both from a conventional EEG cap and from an accelerometer placed on the head of subjects walking at different speeds on a treadmill, we demonstrated that motion artifacts in phase with the

Acknowledgments

M. Duvinage is a FNRS (Fonds National de la Recherche Scientifique) Research Fellow. This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office. The scientific responsibility rests with its author(s).

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