Precise estimation of human corticospinal excitability associated with the levels of motor imagery-related EEG desynchronization extracted by a locked-in amplifier algorithm

Kensho Takahashi,Nobuaki Mizuguchi,Junichi Ushiba,Kenji Kato

doi:10.1186/s12984-018-0440-5

Abstract

BackgroundPhysical motor exercise aided by an electroencephalogram (EEG)-based brain-computer interface (BCI) is known to improve motor recovery in patients with stroke. In such a BCI paradigm, event-related desynchronization (ERD) in the alpha and beta bands extracted from EEG recorded over the primary sensorimotor area (SM1) is often used, since ERD has been suggested to be associated with an increase of corticospinal excitability. Recently, we demonstrated a novel online lock-in amplifier (LIA) algorithm to estimate the amplitude modulation of motor-related SM1 ERD. With this algorithm, the delay time, accuracy, and stability to estimate motor-related SM1 ERD were significantly improved compared with the conventional fast Fourier transformation (FFT) algorithm. These technical improvements to extract an ERD trace imply a potential advantage for a better trace of the excitatory status of the SM1 in a BCI context. Therefore, the aim of this study was to assess the precision of LIA-based ERD tracking for estimation of corticospinal excitability using a transcranial magnetic stimulation (TMS) paradigm.MethodsThe motor evoked potentials (MEPs) induced by single-pulse TMS over the primary motor cortex depending on the magnitudes of SM1 ERD (i.e., 35% and 70%) extracted by the online LIA or FFT algorithm were monitored during a motor imagery task of wrist extension in 17 healthy participants. Then, the peak-to-peak amplitudes of MEPs and their variabilities were assessed to investigate the precision of the algorithms.ResultsWe found greater MEP amplitude evoked by single-pulse TMS triggered by motor imagery-related alpha SM1 ERD than at rest. This enhancement was associated with the magnitude of ERD in both FFT and LIA algorithms. Moreover, we found that the variabilities of peak-to-peak MEP amplitudes at 35% and 70% ERDs calculated by the novel online LIA algorithm were smaller than those extracted using the conventional FFT algorithm.ConclusionsThe present study demonstrated that the calculation of motor imagery-related SM1 ERDs using the novel online LIA algorithm led to a more precise estimation of corticospinal excitability than when the ordinary FFT-based algorithm was used.

Highlights

Physical motor exercise aided by an electroencephalogram (EEG)-based brain-computer interface (BCI) is known to improve motor recovery in patients with stroke
The representative data (Subject 9) of topographic maps of the averaged motor imagery-related primary sensorimotor area (SM1) event-related desynchronization (ERD) over 25 trials obtained from 128-channel EEG signals over the most reactive frequency were further shown in Conditions 1–5 during rest (Fig. 2Aa) and when the ERDs reached 35% and 70% calculated by the online Fourier transformation (FFT) and lock-in amplifier (LIA) algorithms during the kinesthetic motor imagery task (Fig. 2Ab-e)
The SD of normalized peak-to-peak motor evoked potentials (MEPs) amplitudes in Condition 5 “LIA, ERD70%” was smaller than that in Condition 3 “FFT, ERD70%” (p < 0.05, Fig. 4). These results suggest that the variabilities of MEP amplitudes at both levels of ERD magnitude (i.e., ERD 35% and ERD 70%) extracted by the novel online LIA algorithm were reduced compared to those extracted by the conventional FFT algorithm

Summary

Introduction

Physical motor exercise aided by an electroencephalogram (EEG)-based brain-computer interface (BCI) is known to improve motor recovery in patients with stroke In such a BCI paradigm, event-related desynchronization (ERD) in the alpha and beta bands extracted from EEG recorded over the primary sensorimotor area (SM1) is often used, since ERD has been suggested to be associated with an increase of corticospinal excitability. Physical motor exercise aided by an electroencephalogram (EEG)-based brain-computer interface (BCI) facilitates functional recovery in patients with motor deficits due to stroke [1,2,3,4,5,6,7,8,9,10,11,12] In such a BCI paradigm, event-related desynchronization (ERD) in the alpha (8–13 Hz) and beta (15–30 Hz) bands is extracted from EEG signals recorded over the primary sensorimotor area (SM1), and visual and sensory feedback contingent to the extent of ERD is provided via a motor-driven orthosis or neuromuscular electrical stimulation. A variety of spectral analyses, such as fast Fourier transformation (FFT) [8, 10, 12, 16,17,18,19,20], continuous Wavelet transformation [21, 22], an autoregressive model [9, 23, 33], have been used to calculate the frequency spectrum in a given time-sliding window with certain overlaps, but the results were smoothed due to window overlapping, and were delayed due to window length, causing the inevitable limited resolution of ERD

Objectives

Methods

Results

Discussion

Conclusion