The "state of the art" of intraoperative neurophysiological monitoring: An Italian neurosurgical survey.

Introduction: Intraoperative Neurophysiological Monitoring (IOM) is widely used in neurosurgery but specific guidelines are lacking. Therefore, we can assume differences in IOM application between Neurosurgical centers. Research question: The section of Functional Neurosurgery of the Italian Society of Neurosurgery realized a survey aiming to obtain general data on the current practice of IOM in Italy. Materials and methods: A 22-item questionnaire was designed focusing on: volume procedures, indications, awake surgery, experience, organization and equipe. The questionnaire has been sent to Italian Neurosurgery centers. Results: A total of 54 centers completed the survey. The annual volume of surgeries range from 300 to 2000, and IOM is used in 10-20% of the procedures. In 46% of the cases is a neurologist or a neurophysiologist who performs IOM. For supra-tentorial pathology, almost all perform MEPs (94%) SSEPs (89%), direct cortical stimulation (85%). All centers perform IOM in spinal surgery and 95% in posterior fossa surgery. Among the 50% that perform peripheral nerve surgery, all use IOM. Awake surgery is performed by 70% of centers. The neurosurgeon is the only responsible for IOM in 35% of centers. In 83% of cases IOM implementation is adequate to the request. Discussion and conclusions: The Italian Neurosurgical centers perform IOM with high level of specialization, but differences exist in organization, techniques, and expertise. Our survey provides a snapshot of the state of the art in Italy and it could be a starting point to implement a consensus on the practice of IOM.

Rage against the machine? Framing societal threat and efficacy in YouTube videos about artificial intelligence.

Artificial intelligence (AI) has become a part of the mainstream public discourse beyond expert communities about its risks, benefits, and need for regulation. In particular, since 2014, the news media have intensified their coverage of this emerging technology and its potential impact on most domains of society. Although many studies have analyzed traditional media coverage of AI, analyses of social media, especially video-sharing platforms, are rare. In addition, research from a risk communication perspective remains scarce, despite the widely recognized potential threats to society from many AI applications. This study aims to detect recurring patterns of societal threat/efficacy in YouTube videos, analyze their main sources, and compare detected frames in terms of reach and response. Using a theoretical framework combining framing and risk communication, the study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI temporarily peaked (2018). Four dominant AI frames were identified: the balanced frame, the high-efficacy frame, the high-threat frame, and the no-threat frame. The balanced and no-threat frames were the most prevalent, with predominantly positive and neutral AI narratives that neither adequately address the risks nor the necessary societal response from a normative risk communication perspective. The results revealed the specific risks and benefits of AI that are most frequently addressed. Video views and user engagement with AI videos were analyzed. Recommendations for effective AI risk communication and implications for risk governance were derived from the results.

Decoding of unimanual and bimanual reach-and-grasp actions from EMG and IMU signals in persons with cervical spinal cord injury.

Objective. Chronic motor impairments of arms and hands as the consequence of a cervical spinal cord injury (SCI) have a tremendous impact on activities of daily life. A considerable number of people however retain minimal voluntary motor control in the paralyzed parts of the upper limbs that are measurable by electromyography (EMG) and inertial measurement units (IMUs). An integration into human-machine interfaces (HMIs) holds promise for reliable grasp intent detection and intuitive assistive device control.Approach. We used a multimodal HMI incorporating EMG and IMU data to decode reach-and-grasp movements of groups of persons with cervical SCI (n = 4) and without (control, n = 13). A post-hoc evaluation of control group data aimed to identify optimal parameters for online, co-adaptive closed-loop HMI sessions with persons with cervical SCI. We compared the performance of real-time, Random Forest-based movement versus rest (2 classes) and grasp type predictors (3 classes) with respect to their co-adaptation and evaluated the underlying feature importance maps.Main results. Our multimodal approach enabled grasp decoding significantly better than EMG or IMU data alone (p<0.05). We found the 0.25 s directly prior to the first touch of an object to hold the most discriminative information. Our HMIs correctly predicted 79.3 ± STD 7.4 (102.7 ± STD 2.3 control group) out of 105 trials with grand average movement vs. rest prediction accuracies above 99.64% (100% sensitivity) and grasp prediction accuracies of 75.39 ± STD 13.77% (97.66 ± STD 5.48% control group). Co-adaption led to higher prediction accuracies with time, and we could identify adaptions in feature importances unique to each participant with cervical SCI.Significance. Our findings foster the development of multimodal and adaptive HMIs to allow persons with cervical SCI the intuitive control of assistive devices to improve personal independence.

Intermediate gray matter interneurons in the lumbar spinal cord play a critical and necessary role in coordinated locomotion.

Locomotion is a complex task involving excitatory and inhibitory circuitry in spinal gray matter. While genetic knockouts examine the function of individual spinal interneuron (SpIN) subtypes, the phenotype of combined SpIN loss remains to be explored. We modified a kainic acid lesion to damage intermediate gray matter (laminae V-VIII) in the lumbar spinal enlargement (spinal L2-L4) in female rats. A thorough, tailored behavioral evaluation revealed deficits in gross hindlimb function, skilled walking, coordination, balance and gait two weeks post-injury. Using a Random Forest algorithm, we combined these behavioral assessments into a highly predictive binary classification system that strongly correlated with structural deficits in the rostro-caudal axis. Machine-learning quantification confirmed interneuronal damage to laminae V-VIII in spinal L2-L4 correlates with hindlimb dysfunction. White matter alterations and lower motoneuron loss were not observed with this KA lesion. Animals did not regain lost sensorimotor function three months after injury, indicating that natural recovery mechanisms of the spinal cord cannot compensate for loss of laminae V-VIII neurons. As gray matter damage accounts for neurological/walking dysfunction in instances of spinal cord injury affecting the cervical or lumbar enlargement, this research lays the groundwork for new neuroregenerative therapies to replace these lost neuronal pools vital to sensorimotor function.

The Global Study of COVID News: Scope, Findings, and Implications of Quantitative Content Analyses of the COVID-19 News Coverage in the First Two Years of the Pandemic.

Researchers and practitioners have unanimously acknowledged the impact of legacy media coverage of past pandemics as well as COVID-19 and its importance for health-related risk communication. Therefore, this study provides scholars and health communication practitioners with a deeper understanding of the patterns, main themes, and limitations of media reporting and peer-reviewed research in the early stages of the COVID-19 pandemic in different national media environments. Because the objective is to evaluate patterns, this paper focuses on early quantitative and automated content analyses for theoretical contribution, geographic diversity, methodological rigor, and inclusion of risk and crisis communication theory. It also assesses whether authors deduced implications, for both theory and practice of health-related risk and crisis communication. We conducted a content analysis of 66 studies in peer-reviewed journals from the beginning of the pandemic until April 2022. The findings demonstrate that early quantitative analyses of the news coverage of COVID-19 are often not theory-driven, apply heterogeneous forms of framing analysis, and lack references to risk and crisis communication theory. Consequently, only few implications for health communication practice during pandemics were drawn. However, there is evidence of improvement in geographic scope compared to previous research. The discussion addresses the importance of developing a consistent approach to framing analyses of risk and crisis media coverage and the importance of well-designed cross-cultural research in a global pandemic.

Deficiency of B vitamins leads to cholesterol-independent atherogenic transformation of the aorta.

Atherosclerosis, the leading cause of cardiovascular disease responsible for the majority of deaths worldwide, cannot be sufficiently explained by established risk factors, including hypercholesterolemia. Elevated plasma homocysteine is an independent risk factor for atherosclerosis and is strongly linked to cardiovascular mortality. However, the role of homocysteine in atherosclerosis is still insufficiently understood. Previous research in this area has been also hampered by the lack of reproducible in vivo models of atherosclerosis that resemble the human situation. Here, we have developed and applied an automated system for vessel wall injury that leads to more homogenous damage and more pronounced atherosclerotic plaque development, even at low balloon pressure. Our automated system helped to glean vital details of cholesterol-independent changes in the aortic wall of balloon-injured rabbits. We show that deficiency of B vitamins, which are required for homocysteine degradation, leads to atherogenic transformation of the aorta resulting in accumulation of macrophages and lipids, impairment of its biomechanical properties and disorganization of aortic collagen/elastin in the absence of hypercholesterolemia. A combination of B vitamin deficiency and hypercholesterolemia leads to thickening of the aorta, decreased aortic water diffusion, increased LDL-cholesterol and impaired vascular reactivity compared to any single condition. Our findings suggest that deficiency of B vitamins leads to atherogenic transformation of the aorta even in the absence of hypercholesterolemia and aggravates atherosclerosis development in its presence.

Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals.

Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain-computer interfaces (BCIs) for people with motor disabilities.Objective.The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG features, concretely on movement-related cortical potentials. The paradigm must be easily transferable to people without any residual upper-limb movement function and the BCI must be independent of upper-limb movement onset measurements and external cues.Approach. In a study with non-disabled participants, we evaluated a novel BCI paradigm to detect self-initiated reach-and-grasp movements. Two experimental conditions were involved. In one condition, participants performed reach-and-grasp movements to a target and simultaneously shifted their gaze towards it. In a control condition, participants solely shifted their gaze towards the target (oculomotor task). The participants freely decided when to initiate the tasks. After eye artefact correction, the EEG signals were time-locked to the saccade onset and the resulting amplitude features were exploited on a hierarchical classification approach to detect movement asynchronously.Main results. With regards to BCI performance, 54.1% (14.4% SD) of the movements were correctly identified, and all participants achieved a performance above chance-level (around 12%). An average of 21.5% (14.1% SD) of the oculomotor tasks were falsely detected as upper-limb movement. In an additional rest condition, 1.7 (1.6 SD) false positives per minute were measured. Through source imaging, movement information was mapped to sensorimotor, posterior parietal and occipital areas.Significance. We present a novel approach for movement detection using EEG signals which does not rely on upper-limb movement onset measurements or on the presentation of external cues. The participants' behaviour closely matches the natural behaviour during goal-directed reach-and-grasp movements, which also constitutes an advantage with respect to current BCI protocols.

Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems.

Reaching and grasping is an essential part of everybody's life, it allows meaningful interaction with the environment and is key to independent lifestyle. Recent electroencephalogram (EEG)-based studies have already shown that neural correlates of natural reach-and-grasp actions can be identified in the EEG. However, it is still in question whether these results obtained in a laboratory environment can make the transition to mobile applicable EEG systems for home use. In the current study, we investigated whether EEG-based correlates of natural reach-and-grasp actions can be successfully identified and decoded using mobile EEG systems, namely the water-based EEG-Versatile TM system and the dry-electrodes EEG-Hero TM headset. In addition, we also analyzed gel-based recordings obtained in a laboratory environment (g.USBamp/g.Ladybird, gold standard), which followed the same experimental parameters. For each recording system, 15 study participants performed 80 self-initiated reach-and-grasp actions toward a glass (palmar grasp) and a spoon (lateral grasp). Our results confirmed that EEG-based correlates of reach-and-grasp actions can be successfully identified using these mobile systems. In a single-trial multiclass-based decoding approach, which incorporated both movement conditions and rest, we could show that the low frequency time domain (LFTD) correlates were also decodable. Grand average peak accuracy calculated on unseen test data yielded for the water-based electrode system 62.3% (9.2% STD), whereas for the dry-electrodes headset reached 56.4% (8% STD). For the gel-based electrode system 61.3% (8.6% STD) could be achieved. To foster and promote further investigations in the field of EEG-based movement decoding, as well as to allow the interested community to make their own conclusions, we provide all datasets publicly available in the BNCI Horizon 2020 database (http://bnci-horizon-2020.eu/database/data-sets).

Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals.

Eye movements and blinks contaminate electroencephalographic (EEG) and magnetoencephalographic (MEG) activity. As the eye moves, the corneo-retinal dipole (CRD) and eyelid introduce potential/field changes in the M/EEG activity. These eye artifacts can affect a brain-computer interface and thereby impinge on neurofeedback quality. Here, we introduce the sparse generalized eye artifact subspace subtraction (SGEYESUB) algorithm that can correct these eye artifacts offline and in real time. We provide an open source reference implementation of the algorithm and the paradigm to obtain calibration data. Once the algorithm is fitted to calibration data (approx. 5 â€‹min), the eye artifact correction reduces to a matrix multiplication. We compared SGEYESUB with 4 state-of-the-art algorithms using M/EEG activity of 69 participants. SGEYESUB achieved the best trade-off between correcting the eye artifacts and preserving brain activity. Residual correlations between the corrected M/EEG channels and the eye artifacts were below 0.1. Error-related and movement-related cortical potentials were attenuated by less than 0.5 â€‹µV. Our results furthermore demonstrate that CRD and eyelid-related artifacts can be assumed to be stationary for at least 1-1.5 â€‹h, validating the feasibility of our approach in offline and online eye artifact correction.

Perturbation-evoked potentials can be classified from single-trial EEG.

OBJECTIVE: Loss of balance control can have serious consequences on interaction between humans and machines as well as the general well-being of humans. Perceived balance perturbations are always accompanied by a specific cortical activation, the so-called perturbation-evoked potential (PEP). In this study, we investigate the possibility to classify PEPs from ongoing EEG. APPROACH: Fifteen healthy subjects were exposed to seated whole-body perturbations. Each participant performed 120 trials; they were rapidly tilted to the right and left, 60 times respectively. MAIN RESULTS: We achieved classification accuracies of more than 85% between PEPs and rest EEG using a window-based classification approach. Different window lengths and electrode layouts were compared. We were able to achieve excellent classification performance (87.6 ± 8.0% accuracy) by using a short window length of 200 ms and a minimal electrode layout consisting of only the Cz electrode. The peak classification accuracy coincides in time with the strongest component of PEPs, called N1. SIGNIFICANCE: We showed that PEPs can be discriminated against ongoing EEG with high accuracy. These findings can contribute to the development of a system that can detect balance perturbations online.

Decoding hand movements from human EEG to control a robotic arm in a simulation environment.

OBJECTIVE: Daily life tasks can become a significant challenge for motor impaired persons. Depending on the severity of their impairment, they require more complex solutions to retain an independent life. Brain-computer interfaces (BCIs) are targeted to provide an intuitive form of control for advanced assistive devices such as robotic arms or neuroprostheses. In our current study we aim to decode three different executed hand movements in an online BCI scenario from electroencephalographic (EEG) data. APPROACH: Immersed in a desktop-based simulation environment, 15 non-disabled participants interacted with virtual objects from daily life by an avatar's robotic arm. In a short calibration phase, participants performed executed palmar and lateral grasps and wrist supinations. Using this data, we trained a classification model on features extracted from the low frequency time domain. In the subsequent evaluation phase, participants controlled the avatar's robotic arm and interacted with the virtual objects in case of a correct classification. MAIN RESULTS: On average, participants scored online 48% of all movement trials correctly (3-condition scenario, adjusted chance level 40%, alpha = 0.05). The underlying movement-related cortical potentials (MRCPs) of the acquired calibration data show significant differences between conditions over contralateral central sensorimotor areas, which are retained in the data acquired from the online BCI use. SIGNIFICANCE: We could show the successful online decoding of two grasps and one wrist supination movement using low frequency time domain features of the human EEG. These findings can potentially contribute to the development of a more natural and intuitive BCI-based control modality for upper limb motor neuroprostheses or robotic arms for people with motor impairments.

Unimanual and Bimanual Reach-and-Grasp Actions Can Be Decoded From Human EEG.

While most tasks of daily life can be handled through a small number of different grasps, many tasks require the action of both hands. In these bimanual tasks, the second hand has either a supporting role (e.g. for fixating a jar) or a more active role (e.g. grasping a pot on both handles). In this study we attempt to discriminate the neural correlates of unimanual (performed with left and right hand) from bimanual reach-and-grasp actions using the low-frequency time-domain electroencephalogram (EEG). In a self-initiated movement task, 15 healthy participants were asked to perform unimanual (palmar and lateral grasps with left and right hand) and bimanual (double lateral, mixed palmar/lateral) reach-and-grasps on objects of daily life. Using EEG time-domain features in the frequency range of 0.3-3 Hz, we achieved multiclass-classification accuracies of 38.6 ± 6.6% (7 classes, 17.1% chance level) for a combination of 6 movements and 1 rest condition. The grand average confusion matrix shows highest true positive rates (TPR) for the rest (63%) condition while TPR for the movement classes varied between 33 to 41%. The underlying movement-related cortical potentials (MRCPs) show significant differences between unimanual (e.g left hand vs. right hand grasps) as well unimanual vs. bimanual conditions which both can be attributed to lateralization effects. We believe that these findings can be exploited and further used for attempts in providing persons with spinal cord injury a form of natural control for bimanual neuroprostheses.

Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs.

For Brain-Computer interfaces (BCIs), system calibration is a lengthy but necessary process for successful operation. Co-adaptive BCIs aim to shorten training and imply positive motivation to users by presenting feedback already at early stages: After just 5 min of gathering calibration data, the systems are able to provide feedback and engage users in a mutual learning process. In this work, we investigate whether the retraining stage of co-adaptive BCIs can be adapted to a semi-supervised concept, where only a small amount of labeled data is available and all additional data needs to be labeled by the BCI itself. The aim of the current work was to evaluate whether a semi-supervised co-adaptive BCI could successfully compete with a supervised co-adaptive BCI model. In a supporting two-class (190 trials per condition) BCI study based on motor imagery tasks, we evaluated both approaches in two separate groups of 10 participants online, while we simulated the other approach in each group offline. Our results indicate that despite the lack of true labeled data, the semi-supervised driven BCI did not perform significantly worse (p > 0.05) than the supervised counterpart. We believe that these findings contribute to developing BCIs for long-term use, where continuous adaptation becomes imperative for maintaining meaningful BCI performance. Graphical abstract In this work, we investigate whether the retraining stage of a co-adaptive BCI can be adapted to a semi-supervised concept, where only a small amount of labeled data is available and all additional data needs to be labeled by the BCI itself. In two groups of 10 persons, we evaluate a supervised as well as a semi-supervised approach. Our results indicate that despite the lack of true labeled data, the semi-supervised driven BCI did not perform significantly worse (p > 0.05) than the supervised counterpart.

Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury.

We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).

Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions.

Brain-computer interfaces (BCIs) might provide an intuitive way for severely motor impaired persons to operate assistive devices to perform daily life activities. Recent studies have shown that complex hand movements, such as reach-and-grasp tasks, can be decoded from the low frequency of the electroencephalogram (EEG). In this work we investigated whether additional features extracted from the frequency-domain of alpha and beta bands could improve classification performance of rest vs. palmar vs. lateral grasp. We analysed two multi-class classification approaches, the first using features from the low frequency time-domain, and the second in which we combined the time-domain with frequency-domain features from alpha and beta bands. We measured EEG of ten participants without motor disability which performed self-paced reach-and-grasp actions on objects of daily life. For the time-domain classification approach, participants reached an average peak accuracy of 65%. For the combined approach, an average peak accuracy of 75% was reached. In both approaches and for all subjects, performance was significantly higher than chance level (38.1%, 3-class scenario). By computing the confusion matrices as well as feature rankings through the Fisher score, we show that movement vs. rest classification performance increased considerably in the combined approach and was the main responsible for the multi-class higher performance. These findings could help the development of BCIs in real-life scenarios, where decreasing false movement detections could drastically increase the end-user acceptance and usability of BCIs.

Applying intuitive EEG-controlled grasp neuroprostheses in individuals with spinal cord injury: Preliminary results from the MoreGrasp clinical feasibility study.

The aim of the MoreGrasp project is to develop a non-invasive, multimodal user interface including a brain-computer interface (BCI) for control of a grasp neuroprostheses in individuals with high spinal cord injury (SCI). The first results of the ongoing MoreGrasp clinical feasibility study involving end users with SCI are presented. This includes BCI screening sessions, in which we investigate the electroencephalography (EEG) patterns associated with single, natural movements of the upper limb. These patterns will later be used to control the neuroprosthesis. Additionally, the MoreGrasp grasp neuroprosthesis consisting of electrode arrays embedded in an individualized textile forearm sleeve is presented. The general feasibility of this electrode array in terms of corrections of misalignments during donning is shown together with the functional results in end users of the electrode forearm sleeve.

A Survey on Pituitary Surgery in Italy.

BACKGROUND: Pituitary tumors are a heterogeneous group of lesions that are usually benign. Therefore, a proper understanding of the anatomy, physiology, and pathology is mandatory to achieve favorable outcomes. Accordingly, diagnostic tests and treatment guidelines should be determined and implemented. Thus, we decided to perform a multicenter study among Italian neurosurgical centers performing pituitary surgery to provide an actual depiction from the neurosurgical standpoint. METHODS: On behalf of the SINch (Società Italiana di Neurochirurgia), a survey was undertaken with the participants to explore the activities in the field of pituitary surgery within 41 public institutions. RESULTS: Of the 41 centers, 37 participated in the present study. The total number of neurosurgical procedures performed in 2016 was 1479. Most of the procedures were performed using the transsphenoidal approach (1320 transsphenoidal [1204 endoscopic, 53 microscopic, 53 endoscope-assisted microscopic] vs. 159 transcranial). A multidisciplinary tumor board is convened regularly in 32 of 37 centers, and a research laboratory is present in 18 centers. CONCLUSIONS: Diagnosing pituitary/hypothalamus disorders and treating them is the result of teamwork, composed of several diverse experts. Regarding neurosurgery, our findings have confirmed the central role of the transsphenoidal approach, with preference toward the endoscopic technique. Better outcomes can be expected at centers with a multidisciplinary team and a full, or part of a, residency program, with a greater surgical caseload.

Microvascular Decompression of Nervus Intermedius.

Nervus intermedius neuralgia is an extremely rare craniofacial neuralgia characterized by intermittent episodes of pain located deep in the ear that last for seconds or minutes and are often triggered by sensory or mechanical stimuli at the posterior wall of the auditory canal without any underlying pathology. Pain can be associated with disorders of lacrimation, salivation, and taste. Despite the fact that the majority of cases is idiopathic, reports can be found in the literature, where this neuralgia is secondary to a neurovascular conflict between the seventh cranial nerve and anterior-inferior cerebellar artery, posterior-inferior cerebellar artery, and vertebral artery or their branches. For these cases a microvascular decompression procedure can be considered a valid therapeutic approach. In a video, we describe microsurgical decompression of the nervus intermedius in a 40-year-old lady who described a 19-year history of short-lasting paroxystic pain felt in the deep external acoustic meatus on the left side, refractory to medical treatment, with no disturbances of lacrimation, salivation, or taste.

Factors that affect error potentials during a grasping task: toward a hybrid natural movement decoding BCI.

OBJECTIVE: In this manuscript, we consider factors that may affect the design of a hybrid brain-computer interface (BCI). We combine neural correlates of natural movements and interaction error-related potentials (ErrP) to perform a 3D reaching task, focusing on the impact that such factors have on the evoked ErrP signatures and in their classification. APPROACH: Users attempted to control a 3D virtual interface that simulated their own hand, to reach and grasp two different objects. Three factors of interest were modulated during the experimentation: (1) execution speed of the grasping, (2) type of grasping and (3) mental strategy (motor imagery or real motion) used to produce motor commands. Thirteen healthy subjects carried out the protocol. The peaks and latencies of the ErrP were analyzed for the different factors as well as the classification performance. MAIN RESULTS: ErrP are evoked for erroneous commands decoded from neural correlates of natural movements. The analysis of variance (ANOVA) analyses revealed that latency and magnitude of the most characteristic ErrP peaks were significantly influenced by the speed at which the grasping was executed, but not the type of grasp. This resulted in an greater accuracy of single-trial decoding of errors for fast movements (75.65%) compared to slow ones (68.99%). SIGNIFICANCE: Understanding the effects of combining paradigms is a first step to design hybrid BCI that optimize decoding accuracy and can be deployed in motor substitution and neuro-rehabilitation applications.