# Predicting the Onset of Freezing of Gait Using EEG Dynamics
###### Abstract  
Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson's disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to \"stop\". The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping. freezing of gait; Parkinson's disease; voluntary stopping; convolutional neural network; EEGNet; Shallow ConvNet; Deep ConvNet +
Footnote †: journal: Medical  
FOG detection is still a widely researched topic, with attempts made by several combinations of devices and algorithms. The first automatic detection of FOG was proposed by [PERSON] et al. [6] using frequency-based features from accelerometer signals. This work was extended to improve the detection accuracy and developed as a FOG monitoring system with smartphones and wearable accelerometers [7]. Accelerometers [8; 9], gyroscopes [8] and inertial data [10; 11] have been employed for automatic FOG detection [9]. Several researchers attempted the early detection of FOG as it benefits intervention strategies. [PERSON] et al. [12] detected the onset of FOG based on EEG wavelet energy and entropy features. The onset of FOG was also detected by a sensor placed on the lower limb of the patient [13]. Electrocardiogram and skin conductance were used to predict the onset of FOG [14]. An efficient FOG detection system should not only be able to predict the onset of FOG, but it should also be able to distinguish involuntary stopping from the transition to voluntary stopping. The potential of brain dynamics in discerning the onset of freezing in PD patients has already been established [12]. However, earlier researchers relied on a 5 s window to discern the transition to freezing from normal walking [15; 16]. Recently, we discovered that EEG signatures for transition to FOG are distinct from the intention to stop [17]. We observed an increase in the delta, theta, and beta power at the central region during the transition to freezing compared to normal walking. The transition to voluntary stopping was observed to show increased EEG power at the frontal, central, parietal, and occipital regions compared to the transition to FOG. Accurate detection of the transition to FOG from potential confounding transitions to voluntary stopping and normal walking is still challenging because of the complexity of designing handcrafted features. In order to determine the differences in brain activity during the transition to freezing and the transition to stopping, we employed the classical timed up-and-go (TUG) task [18] in our experiment. The TUG protocol involves the participants starting from a sitting position to standing before walking towards an identified point and then turning back there to return to the starting position.