A Transfer Learning Approach for UAV Path Design with Connectivity Outage Constraint

The connectivity-aware path design is crucial in the effective deployment of autonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning (RL) algorithms have become the popular approach to solving this type of complex problem, but RL algorithms suffer slow convergence. In this paper, we propose a Transfer Learning (TL) approach, where we use a teacher policy previously trained in an old domain to boost the path learning of the agent in the new domain. As the exploration processes and the training continue, the agent refines the path design in the new domain based on the subsequent interactions with the environment. We evaluate our approach considering an old domain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacher path policy, previously trained at sub-6 GHz path, is the solution to a connectivity-aware path problem that we formulate as a constrained Markov Decision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network (DQN) to solve the path design at sub-6 GHz that guarantees connectivity constraint satisfaction. We empirically demonstrate the effectiveness of our approach for different urban environment scenarios. The results demonstrate that our proposed approach is capable of reducing the training time considerably at mmWave.Comment: 14 pages,8 figures, journal pape

Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface

Objective. Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning. Approach. The proposed framework combines the subject-specific covariance matrix (Σss) estimated using the few available trials from the new subject, with a novelDTW-based transferred covariance matrix (ΣDTW) estimated using previous subjects' trials. In the proposedΣDTW, the available labelled trials from the previous subjects are temporally aligned to the average of the few available trials of the new subject from the same class using DTW. This alignment aims to reduce temporal variations and non-stationarities between previous subjects trials and the available few trials from the new subjects. Moreover, to tackle the problem of regularization parameter selection when only few trials are available for training, an online method is proposed, where the best regularization parameter is selected based on the confidence scores of the trained classifier on upcoming first few labelled testing trials. Main results. The proposed framework is evaluated on two datasets against two baseline algorithms. The obtained results reveal that DTW-RCSP significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training. Significance. Impressively, our results show that successful BCI interactions could be achieved with a calibration session as small as only one trial per class

Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography

Numerous sleep disorders are characterised by movement during sleep, these include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb movement disorder. The process of diagnosing movement related sleep disorders requires laborious and time-consuming visual analysis of sleep recordings. This process involves sleep clinicians visually inspecting electromyogram (EMG) signals to identify abnormal movements. The distribution of characteristics that represent movement can be diverse and varied, ranging from brief moments of tensing to violent outbursts. This study proposes a framework for automated limb-movement detection by fusing data from two EMG sensors (from the left and right limb) through a Dirichlet process mixture model. Several features are extracted from 10 second mini-epochs, where each mini-epoch has been classified as 'leg-movement' or 'no leg-movement' based on annotations of movement from sleep clinicians. The distributions of the features from each category can be estimated accurately using Gaussian mixture models with the Dirichlet process as a prior. The available dataset includes 36 participants that have all been diagnosed with RBD. The performance of this framework was evaluated by a 10-fold cross validation scheme (participant independent). The study was compared to a random forest model and outperformed it with a mean accuracy, sensitivity, and specificity of 94\%, 48\%, and 95\%, respectively. These results demonstrate the ability of this framework to automate the detection of limb movement for the potential application of assisting clinical diagnosis and decision-making

Opportunities and obstacles in non-invasive brain stimulation

Non-invasive brain stimulation (NIBS) is a complex and multifaceted approach to modulating brain activity and holds the potential for broad accessibility. This work discusses the mechanisms of the four distinct approaches to modulating brain activity non-invasively: electrical currents, magnetic fields, light, and ultrasound. We examine the dual stochastic and deterministic nature of brain activity and its implications for NIBS, highlighting the challenges posed by inter-individual variability, nebulous dose-response relationships, potential biases and neuroanatomical heterogeneity. Looking forward, we propose five areas of opportunity for future research: closed-loop stimulation, consistent stimulation of the intended target region, reducing bias, multimodal approaches, and strategies to address low sample sizes

A multimodal neuroprosthetic interface to record, modulate and classify electrophysiological biomarkers relevant to neuropsychiatric disorders

Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders

Prefrontal modulation of the sustained attention network in ageing, a tDCS-EEG co-registration approach

The ability to sustain attention is integral to healthy cognition in aging. The right PFC (rPFC) is critical for maintaining high levels of attentional focus. Whether plasticity of this region can be harnessed to support sustained attention in older adults is unknown. We used transcranial direct current stimulation to increase cortical excitability of the rPFC, while monitoring behavioral and electrophysiological markers of sustained attention in older adults with suboptimal sustained attention capacity. During rPFC transcranial direct current stimulation, fewer lapses of attention occurred and electroencephalography signals of frontal engagement and early visual attention were enhanced. To further verify these results, we repeated the experiment in an independent cohort of cognitively typical older adults using a different sustained attention paradigm. Again, prefrontal stimulation was associated with better sustained attention. These experiments suggest the rPFC can be manipulated in later years to increase top–down modulation over early sensory processing and improve sustained attention performance. This holds valuable information for the development of neurorehabilitation protocols to ameliorate age-related deficits in this capacity

Familiarity Affects Entrainment of EEG in Music Listening

Music perception involves complex brain functions. The relationship between music and brain such as cortical entrainment to periodic tune, periodic beat, and music have been well investigated. It has also been reported that the cerebral cortex responded more strongly to the periodic rhythm of unfamiliar music than to that of familiar music. However, previous works mainly used simple and artificial auditory stimuli like pure tone or beep. It is still unclear how the brain response is influenced by the familiarity of music. To address this issue, we analyzed electroencelphalogram (EEG) to investigate the relationship between cortical response and familiarity of music using melodies produced by piano sounds as simple natural stimuli. The cross-correlation function averaged across trials, channels, and participants showed two pronounced peaks at time lags around 70 ms and 140 ms. At the two peaks the magnitude of the cross-correlation values were significantly larger when listening to unfamiliar and scrambled music compared to those when listening to familiar music. Our findings suggest that the response to unfamiliar music is stronger than that to familiar music. One potential application of our findings would be the discrimination of listeners’ familiarity with music, which provides an important tool for assessment of brain activity

Facilitating motor imagery-based brain–computer interface for stroke patients using passive movement

Motor imagery-based brain–computer interface (MI-BCI) has been proposed as a rehabilitation tool to facilitate motor recovery in stroke. However, the calibration of a BCI system is a time-consuming and fatiguing process for stroke patients, which leaves reduced time for actual therapeutic interaction. Studies have shown that passive movement (PM) (i.e., the execution of a movement by an external agency without any voluntary motions) and motor imagery (MI) (i.e., the mental rehearsal of a movement without any activation of the muscles) induce similar EEG patterns over the motor cortex. Since performing PM is less fatiguing for the patients, this paper investigates the effectiveness of calibrating MI-BCIs from PM for stroke subjects in terms of classification accuracy. For this purpose, a new adaptive algorithm called filter bank data space adaptation (FB-DSA) is proposed. The FB-DSA algorithm linearly transforms the band-pass-filtered MI data such that the distribution difference between the MI and PM data is minimized. The effectiveness of the proposed algorithm is evaluated by an offline study on data collected from 16 healthy subjects and 6 stroke patients. The results show that the proposed FB-DSA algorithm significantly improved the classification accuracies of the PM and MI calibrated models (p < 0.05). According to the obtained classification accuracies, the PM calibrated models that were adapted using the proposed FB-DSA algorithm outperformed the MI calibrated models by an average of 2.3 and 4.5 % for the healthy and stroke subjects respectively. In addition, our results suggest that the disparity between MI and PM could be stronger in the stroke patients compared to the healthy subjects, and there would be thus an increased need to use the proposed FB-DSA algorithm in BCI-based stroke rehabilitation calibrated from PM

Advanced brain computer interface

A brain-computer interface (BCI) provides a new pathway for communication and control through decoding information directly extracted from the neurophysiological signals. In the majority of current BCI systems, brain activity is measured through the electroencephalogram (EEG), due to its low cost and high temporal resolution. However, EEG signals have a poor spatial resolution. A signal of interest is mixed with a set of irrelevant signals from concurrent brain activities while they may have similar activation frequencies and amplitude. Moreover, EEG signals are non-stationary, and they may be distorted by artifacts such as electrooculography (EOG) or electromyography (EMG). These drawbacks can result in inaccurate and deteriorated BCI performances. To overcome these issues and consequently to improve the accuracy and robustness of BCI, we propose four novel and complementary algorithms, namely sparse common spatial patterns (SCSP), optimized sparse spatio-spectral filters (OSSSF), kullback-leibler-based common spatial patterns (KLCSP), and EEG data space adaptation (EEG-DSA). The data acquired from healthy and stroke subjects are used to exemplify the power of the proposed algorithms. The experimental results show that our proposed algorithms improve the accuracy and the robustness of the EEG-based BCI by generating more robust and invariant features, and by adapting the BCI model to non-stationarities. The experimental results also show that our proposed algorithms can potentially improve the usability and practicability of BCI by reducing the set up and calibration time. These improvements will consequently lead to a more intuitive and pleasing interface for daily use.Doctor of Philosophy (SCE