Abstract
Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is mostly considered as emerging from oscillations of the neuronal populations. However, the changes in the temporal dynamics, especially in the broadband arrhythmic EEG have not been investigated for movement intention detection. The Long-Range Temporal Correlations (LRTC) are ubiquitously present in several neuronal processes, typically requiring longer timescales to detect. In this paper, we study the ongoing changes in the dynamics of long- as well as short-range temporal dependencies in the single trial broadband EEG during movement intention. We obtained LRTC in 2 s windows of broadband EEG and modeled it using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model which allowed simultaneous modeling of short- and long-range temporal correlations. There were significant (p < 0.05) changes in both broadband long- and short-range temporal correlations during movement intention and execution. We discovered that the broadband LRTC and narrowband ERD are complementary processes providing distinct information about movement because eliminating LRTC from the signal did not affect the ERD and conversely, eliminating ERD from the signal did not affect LRTC. Exploring the possibility of applications in Brain Computer Interfaces (BCI), we used hybrid features with combinations of LRTC, ARFIMA, and ERD to detect movement intention. A significantly higher (p < 0.05) classification accuracy of 88.3 ± 4.2% was obtained using the combination of ARFIMA and ERD features together, which also predicted the earliest movement at 1 s before its onset. The ongoing changes in the long- and short-range temporal correlations in broadband EEG contribute to effectively capturing the motor command generation and can be used to detect movement successfully. These temporal dependencies provide different and additional information about the movement.
Highlights
Movement is the primary mode of interaction with the environment and studying the neuronal processes involved in movement generation is interesting
We explore the increase in Long-Range Temporal Correlation (LRTC) during the voluntary movement further because it is not practical to identify the exponent for LRTC robustly from either autocorrelation or power spectrum of a non-stationary process such as EEG, especially on single trials
We demonstrated broadband LRTCs as novel neural correlates of movement intention in EEG
Summary
Movement is the primary mode of interaction with the environment and studying the neuronal processes involved in movement generation is interesting. Spectral power changes in the narrowband sensorimotor oscillations in EEG such as Event-Related (De)Synchronization (ERD/S) (Pfurtscheller and Lopes da Silva, 1999) are used to determine movement. The alpha band amplitude envelope shows LRTC, which decreases during movement (LinkenkaerHansen et al, 2004) Despite both the temporal and spectral changes in EEG, narrowband spectral features such as ERD are explored more commonly (Yuan and He, 2014; He et al, 2015), especially for movement detection and its applications in BCI. The LRTC during movement is primarily obtained on the narrowband alpha amplitude envelope of the longer segments of EEG (Linkenkaer-Hansen et al, 2001; Zhigalov et al, 2016).
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