Biology subjects

Lopez-Larraz, E.

Publications and source records attributed to Lopez-Larraz, E..

6 recordsLinked to original sources

bioRxiv2024

EEG-VLM Toolbox: Extending voxel-based lesion mapping to multi-dimensional EEG data

Focal brain lesions (such as with stroke) cause functional changes in local and distributed neural systems. While there is a long history of post-stroke neurophysiological assessment using electroencephalography (EEG), the observed neurophysiological changes have rarely been related to specific lesion locations. Therefore, the relationships between anatomical injury and physiological changes after stroke remain unclear. Voxel-based lesion symptom mapping (VLSM) is a tool for statistically relating stroke lesion locations to "symptoms", but current VLSM methods are restricted to symptoms that can be defined by a single value. Therefore, current VLSM techniques are unable to map the relationships between anatomical injury and multidimensional neurophysiological data such as EEG, which contains rich spatio-temporal information across different channels and frequency bands. Here we present a novel algorithm, EEG Voxel-based Lesion Mapping (EEG-VLM), that produces the set of significant relationships between precise neuroanatomical injury locations and neurophysiology (defined by a cluster of adjacent EEG channels and frequency bands). Further, the algorithm provides statistical analyses to define the overall significance of each neural structure-function relationship by correcting for multiple comparisons using a permutation test. Applying EEG-VLM to a dataset of recordings from chronic stroke patients performing a cued upper extremity movement task, we found that subjects with lesions in frontal subcortical white matter have reduced ipsilesional parietal cue-evoked EEG responses. These results are consistent with damage to a frontal-parietal network that has been associated with impairments in attention. EEG-VLM is a novel and unbiased method for relating neurophysiologic changes after stroke with neuroanatomic lesions. In the context of focal brain lesions associated with neurological impairments, we propose that this method will enable improved mechanistic understanding, facilitate biomarker development, and guide neurorehabilitation strategies.

neuroscience↗

bioRxiv2024

Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea

Digital therapeutics, enabled by advanced machine learning algorithms and medical wearable devices, offer a promising approach to streamline diagnostics and improve access to healthcare. Within this framework, automatic sleep scoring can provide accurate and efficient sleep analysis from electrophysiological signals recorded with wearable sensors, such as electroencephalography (EEG). However, the optimal configuration and temporal dynamics of automatic sleep scoring systems remain unclear, especially concerning their performance across different population samples. This study systematically investigates the impact of electrode setup, temporal scope, and population characteristics on the performance of automatic sleep scoring algorithms. Utilizing a convolutional neural network (CNN) model, we analyzed various electrode configurations and temporal dynamics using datasets comprising both healthy participants and individuals with sleep apnea. Our findings reveal that sleep scoring based on a single frontal EEG channel demonstrates reliable congruency with human expert scorers, with minimal improvement observed with additional sensors. Moreover, we demonstrate that real-time sleep scoring can be achieved with comparable accuracy to offline methods, which rely on past and future information to classify a window of interest. Remarkably, a notable reduction in decoding accuracy is observed for individuals with sleep disorders compared to healthy participants, highlighting the challenges inherent in accurately assessing sleep stages in clinical populations. Digital solutions for automatic sleep scoring hold promise for facilitating timely diagnoses and personalized treatment plans, with applications extending beyond sleep analysis to include closed-loop neurostimulation interventions. Our findings provide valuable insights into the complexities of automatic sleep scoring and offer considerations for the development of effective and efficient sleep assessment tools in both clinical and research settings.

neuroscience↗

bioRxiv2024

Unveiling Movement Intention after Stroke: Integrating EEG and EMG for Motor Rehabilitation

Detecting attempted movements of a paralyzed limb is a key step for neural interfaces for motor rehabilitation and restoration after a stroke. In this paper, we present a systematic evaluation of electroencephalographic (EEG) and electromyographic (EMG) activity to decode when stroke patients with severe upper-limb paralysis attempt to move their affected arm. EEG and EMG recordings of 35 chronic stroke patients were analyzed. We trained classifiers to discriminate between rest and movement attempt states relying on brain, muscle, or both types of features combined. Our results reveal that: i) EEG and residual EMG features provide complementary information to detect attempted movements, obtaining significantly higher decoding accuracy when both sources of activity are combined; ii) EMG-based, but not EEG-based, decoding accuracy correlates with the degrees of impairment of the patient; and iii) the percentage of patients that achieve decoding accuracy above the chance level strongly depends on the type of features considered, and can be as low as 50% of them if only ipsilesional EEG is used. These results offer new perspectives to develop improved neurotechnologies that establish a more accurate contingent link between the central and peripheral nervous system after a stroke, leveraging Hebbian learning and facilitating functional plasticity and recovery.

neuroscience↗

bioRxiv2023

Garments that measure EEG: Evaluation of an EEG sensor layer fully implemented with smart textiles

This paper presents the first garment capable of measuring EEG activity with accuracy comparable to state-of-the art dry EEG systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) fully implemented as a garment, using threads, fabrics and smart textiles, without relying on any metal or plastic materials. The garment is interfaced via a connector to a mobile EEG amplifier to complete the measurement system. The new EEG system (Garment-EEG) has been characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) electrophysiological measurements (spontaneous and evoked EEG activity); (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more prone to getting affected by artifacts in adverse recording conditions due to poorer contact impedances. Ergonomics and comfort favor the textile-based sensor layer with respect to its metal-based counterpart. User acceptance is the main obstacle for EEG systems to democratize neurotechnology and non-invasive brain-computer interfaces. EEG sensor layers encapsulated in wearables have the potential to enable neurotechnology that is naturally accepted by people in their daily lives. Furthermore, by supporting the EEG implementation in the textile industry it is manufactured with lower cost and much less pollution compared to the metal and plastic industries.

neuroscience↗

bioRxiv2020

Non-invasive brain-spine interface: continuous brain control of trans-spinal magnetic stimulation using EEG

Brain-controlled neuromodulation therapies have emerged as a promising tool to promote functional recovery in patients with motor disabilities. This neuromodulatory strategy is exploited by brain-machine interfaces and could be used for restoring lower limb muscle activity or alleviating gait deficits. Towards a non-invasive approach for leg neurorehabilitation, we present a set-up that combines acquisition of electroencephalographic (EEG) activity to volitionally control trans-spinal magnetic stimulation (ts-MS). We engineered, for the first time, a non-invasive brain-spine interface (BSI) to contingently connect motor cortical activation during leg motor imagery with the activation of leg muscles via ts-MS. This novel brain-controlled stimulation was validated with 10 healthy participants who underwent one session including different ts-MS conditions. After a short screening of their cortical activation during lower limb motor imagery, the participants used the closed-loop system at different stimulation intensities and scored system usability and comfort. We demonstrate the efficiency and robustness of the developed system to remove online stimulation artifacts from EEG regardless of ts-MS intensity used. All the participants reported absence of pain due to ts-MS and good usability. Our results also revealed that ts-MS controlled afferent and efferent intensity-dependent modulation of the nervous system. The here presented system represents a novel non-invasive means to neuromodulate peripheral nerve activity of lower limb using brain-controlled spinal stimulation.

bioengineering↗

bioRxiv2020

Intensity and dose of neuromuscular electrical stimulation influence sensorimotor cortical excitability

Neuromuscular electrical stimulation (NMES) of the peripheral nervous system has been largely used in the field of neurorehabilitation to decrease muscle atrophy and to restore motor function in paralyzed patients. The rehabilitative effects of NMES rely on the direct or indirect efferent effect on muscle tone and afferent volleys that induce cortical excitation. Although different neuroimaging tools suggested the capability of NMES to regulate the excitability of sensorimotor cortex and corticospinal circuits, to date how intensity and dose of NMES can neuromodulate the brain oscillatory activity measured with electroencephalography (EEG) is yet to be clarified. In the present study, we quantify the effect of NMES parameters on brain oscillatory activity of twelve healthy participants who underwent stimulation of wrist extensors during rest while EEG was recorded. Three different NMES intensities were included: (1) low, inducing slight sensory perception, (2) medium, inducing moderate sensory perception, and (3) high, generating a functional movement. Firstly, we efficiently removed stimulation artifacts from the sensorimotor brain oscillatory activity. Secondly, we analyzed the effect of amplitude and dose on the latter. On the one hand, we observed significant NMES amplitude-dependent brain SMR modulation, demonstrating the direct effect of afferent receptors recruitment. On the other hand, our results revealed a significant NMES amplitude-based dose-effect on SMR modulation over time. While at low and medium intensities the NMES produced a significant cortical inhibitory effect in time, at high intensity a significant cortical facilitatory effect was induced. These results highlight the functionally relevant role of muscle contraction and proprioception in sensorimotor processes, which should be carefully considered for the design and development of NMES based neuromodulation.

neuroscience↗