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Query: "author" (Kevin De Pauw) .

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1.
Optimal sensor set for decoding motor imagery from EEG
Arnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uroš Marušič, Sidney Grosprêtre, Ann Nowé, Romain Meeusen, Kevin De Pauw, 2023, original scientific article

Abstract: Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.
Keywords: brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning
Published in DiRROS: 03.04.2023; Views: 773; Downloads: 354
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2.
A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics
Arnau Dillen, Elke Lathouwers, Aleksandar Miladinović, Uroš Marušič, Fakhreddine Ghaffari, Olivier Romain, Romain Meeusen, Kevin De Pauw, 2022, original scientific article

Abstract: Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.
Keywords: neuroprosthetics, brain-computer interface, machine learning, electroencephalography, data-driven learning, lower limb amputation
Published in DiRROS: 21.07.2022; Views: 844; Downloads: 579
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3.
Neural bases of age-related sensorimotor slowing in the upper and lower limbs
Uroš Marušič, Manca Peskar, Kevin De Pauw, Nina Omejc, Gorazd Drevenšek, Bojan Rojc, Rado Pišot, Voyko Kavcic, 2022, original scientific article

Abstract: With advanced age, there is a loss of reaction speed that may contribute to an increased risk of tripping and falling. Avoiding falls and injuries requires awareness of the threat, followed by selection and execution of the appropriate motor response. Using event-related potentials (ERPs) and a simple visual reaction task (RT), the goal of our study was to distinguish sensory and motor processing in the upper- and lower-limbs while attempting to uncover the main cause of age-related behavioral slowing. Strength (amplitudes) as well as timing and speed (latencies) of various stages of stimulus- and motor-related processing were analyzed in 48 healthy individuals (young adults, n = 24, mean age = 34 years; older adults, n = 24, mean age = 67 years). The behavioral results showed a significant age-related slowing, where the younger compared to older adults exhibited shorter RTs for the upper- (222 vs. 255 ms; p = 0.006, respectively) and the lower limb (257 vs. 274 ms; p = 0.048, respectively) as well as lower variability in both modalities (p = 0.001). Using ERP indices, age-related slowing of visual stimulus processing was characterized by overall larger amplitudes with delayed latencies of endogenous potentials in older compared with younger adults. While no differences were found in the P1 component, the later components of recorded potentials for visual stimuli processing were most affected by age. This was characterized by increased N1 and P2 amplitudes and delayed P2 latencies in both upper and lower extremities. The analysis of motor-related cortical potentials (MR) revealed stronger MRCP amplitude for upper- and a non-significant trend for lower limbs in older adults. The MRCP amplitude was smaller and peaked closer to the actual motor response for the upper- than for the lower limb in both age groups. There were longer MRCP onset latencies for lower- compared to upper-limb in younger adults, and a non-significant trend was seen in older adults. Multiple regression analyses showed that the onset of the MRCP peak consistently predicted reaction time across both age groups and limbs tested. However, MRCP rise time and P2 latency were also significant predictors of simple reaction time, but only in older adults and only for the upper limbs. Our study suggests that motor cortical processes contribute most strongly to the slowing of simple reaction time in advanced age. However, late-stage cortical processing related to sensory stimuli also appears to play a role in upper limb responses in the elderly. This process most likely reflects less efficient recruitment of neuronal resources required for the upper and lower extremity response task in older adults.
Keywords: aging, sensoriomotor integration, event-related potential, finger and foot responses, motor-related cortical potential
Published in DiRROS: 04.05.2022; Views: 864; Downloads: 622
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4.
Removal of movement-induced EEG artifacts : current state of the art and guidelines
Daša Gorjan, Klaus Gramann, Kevin De Pauw, Uroš Marušič, 2022, review article

Abstract: Electroencephalography (EEG) is a non-invasive technique used to record cortical neurons' electrical activity using electrodes placed on the scalp. It has become a promising avenue for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG signals are always contaminated by artifacts and other physiological signals. Artifact contamination increases with the intensity of movement. In the last decade (since 2010), researchers have started to implement EEG measurements in dynamic setups to increase the overall ecological validity of the studies. Many different methods are used to remove non-brain activity from the EEG signal, and there are no clear guidelines on which method should be used in dynamic setups and for specific movement intensities. Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis (ICA). However, the choice of method for artifact removal depends on the type and intensity of movement, which affects the characteristics of the artifacts and the EEG parameters of interest. When dealing with EEG under non-static conditions, special care must be taken already in the designing period of an experiment. Software and hardware solutions must be combined to achieve sufficient removal of unwanted signals from EEG measurements. We have provided recommendations for the use of each method depending on the intensity of the movement and highlighted the advantages and disadvantages of the methods. However, due to the current gap in the literature, further development and evaluation of methods for artifact removal in EEG data during locomotion is needed.
Published in DiRROS: 01.03.2022; Views: 794; Downloads: 786
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5.
Passive shoulder exoskeletons : more effective in the lab than in the field?
Sander De Bock, Jo Ghillebert, Renée Govaerts, Shirley A. Elprama, Uroš Marušič, Ben Serrien, An Jacobs, Joost Geeroms, Romain Meeusen, Kevin De Pauw, 2021, original scientific article

Abstract: Shoulder exoskeletons potentially reduce overuse injuries in industrial settings including overhead work or lifting tasks. Previous studies evaluated these devices primarily in laboratory setting, but evidence of their effectiveness outside the lab is lacking. The present study aimed to evaluate the effectiveness of two passive shoulder exoskeletons and explore the transfer of laboratory-based results to the field. Four industrial workers performed controlled and in-field evaluations without and with two exoskeletons, ShoulderX and Skelex in a randomized order. The exoskeletons decreased upper trapezius activity (up to 46%) and heart rate in isolated tasks. In the field, the effects of both exoskeletons were less prominent (up to 26% upper trapezius activity reduction) while lifting windscreens weighing 13.1 and 17.0 kg. ShoulderX received high discomfort scores in the shoulder region and usability of both exoskeletons was moderate. Overall, both exoskeletons positively affected the isolated tasks, but in the field the support of both exoskeletons was limited. Skelex, which performed worse in the isolated tasks compared to ShoulderX, seemed to provide the most support during the in-field situations. Exoskeleton interface improvements are required to improve comfort and usability. Laboratory-based evaluations of exoskeletons should be interpreted with caution, since the effect of an exoskeleton is task specific and not all infield situations with high-level lifting will equally benefit from the use of an exoskeleton. Before considering passive exoskeleton implementation, we recommend analyzing joint angles in the field, because the support is inherently dependent on these angles, and to perform in-field pilot tests. This paper is the first thorough evaluation of two shoulder exoskeletons in a controlled and infield situation.
Keywords: assistive devices, exoskeletons, ergonomics, industrial plants, system validation
Published in DiRROS: 28.02.2022; Views: 966; Downloads: 733
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