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.

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.

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.

EEG neural correlates of goal-directed movement intention.

Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement. In a single-trial classification procedure we found that classification accuracies are enhanced if there is a goal-directed movement in mind. Furthermore, by using the classifier patterns and estimating the corresponding brain sources, we show the importance of motor areas and the additional involvement of the posterior parietal lobule in the discrimination between goal-directed movements and non-goal-directed movements. We discuss next the potential contribution of our results on goal-directed movements to a more reliable brain-computer interface (BCI) control that facilitates recovery in spinal-cord injured or stroke end-users.

Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline.

BACKGROUND: In this work, we share our experiences made at the world-wide first CYBATHLON, an event organized by the Eidgenössische Technische Hochschule Zürich (ETH Zürich), which took place in Zurich in October 2016. It is a championship for severely motor impaired people using assistive prototype devices to compete against each other. Our team, the Graz BCI Racing Team MIRAGE91 from Graz University of Technology, participated in the discipline "Brain-Computer Interface Race". A brain-computer interface (BCI) is a device facilitating control of applications via the user's thoughts. Prominent applications include assistive technology such as wheelchairs, neuroprostheses or communication devices. In the CYBATHLON BCI Race, pilots compete in a BCI-controlled computer game. METHODS: We report on setting up our team, the BCI customization to our pilot including long term training and the final BCI system. Furthermore, we describe CYBATHLON participation and analyze our CYBATHLON result. RESULTS: We found that our pilot was compliant over the whole time and that we could significantly reduce the average runtime between start and finish from initially 178 s to 143 s. After the release of the final championship specifications with shorter track length, the average runtime converged to 120 s. We successfully participated in the qualification race at CYBATHLON 2016, but performed notably worse than during training, with a runtime of 196 s. DISCUSSION: We speculate that shifts in the features, due to the nonstationarities in the electroencephalogram (EEG), but also arousal are possible reasons for the unexpected result. Potential counteracting measures are discussed. CONCLUSIONS: The CYBATHLON 2016 was a great opportunity for our student team. We consolidated our theoretical knowledge and turned it into practice, allowing our pilot to play a computer game. However, further research is required to make BCI technology invariant to non-task related changes of the EEG.

A model for the temporal evolution of the spatial coherence in decaying reverberant sound fields.

Reverberant sound fields are often modeled as isotropic. However, it has been observed that spatial properties change during the decay of the sound field energy, due to non-isotropic attenuation in non-ideal rooms. In this letter, a model for the spatial coherence between two sensors in a decaying reverberant sound field is developed for rectangular rooms. The modeled coherence function depends on room dimensions, surface reflectivity, and orientation of the sensor pair, but is independent of the position of source and sensors in the room. The model includes the spherically isotropic (diffuse) and cylindrically isotropic sound field models as special cases.

Paleoproteomic study of the Iceman's brain tissue.

The Tyrolean Iceman, a Copper-age ice mummy, is one of the best-studied human individuals. While the genome of the Iceman has largely been decoded, tissue-specific proteomes have not yet been investigated. We studied the proteome of two distinct brain samples using gel-based and liquid chromatography-mass spectrometry-based proteomics technologies together with a multiple-databases and -search algorithms-driven data-analysis approach. Thereby, we identified a total of 502 different proteins. Of these, 41 proteins are known to be highly abundant in brain tissue and 9 are even specifically expressed in the brain. Furthermore, we found 10 proteins related to blood and coagulation. An enrichment analysis revealed a significant accumulation of proteins related to stress response and wound healing. Together with atomic force microscope scans, indicating clustered blood cells, our data reopens former discussions about a possible injury of the Iceman's head near the site where the tissue samples have been extracted.

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.

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.

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.

Role of lumbar interspinous distraction on the neural elements.

The interspinous distraction devices are used to treat variable pathologies ranging from facet syndrome, diskogenic low back pain, degenerative spinal stenosis, diskopathy, spondylolisthesis, and instability. The insertion of a posterior element with an interspinous device (ISD) is commonly judged responsive to a relative kyphosis of a lumbar segment with a moderate but persistent increase of the spinal canal and of the foraminal width and area, and without influence on low-grade spondylolisthesis. The consequence is the need of shared specific biomechanical concepts to give for each degenerative problem the right indication through a critical analysis of all available experimental and clinical biomechanical data. We reviewed systematically the available clinical and experimental data about kyphosis, enlargement of the spinal canal, distraction of the interspinous distance, increase of the neural foramina, ligamentous structures, load of the posterior annulus, intradiskal pressure, strength of the spinous processes, degeneration of the adjacent segment, complications, and cost-effectiveness of the ISD. The existing literature does not provide actual scientific evidence over the superiority of the ISD strategy, but most of the experimental and clinical data show a challenging potential. These considerations are applicable with different types of ISD with only few differences between the different categories. Despite--or because of--the low invasiveness of the surgical implantation of the ISD, this technique promises to play a major role in the future degenerative lumbar microsurgery. The main indications for ISD remain lumbar spinal stenoses and painful facet arthroses. A clear documented contraindication is the presence of an anterolisthesis. Nevertheless, the existing literature does not provide evidence of superiority of outcome and cost-effectiveness of the ISD strategy over laminectomy or other surgical procedures. At this time, the devices should be used in clinical randomized independent trials in order to obtain more information concerning the most advantageous optimal indication or, in selected cases, to treat tailored indications.

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).

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.

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.

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.

Epilepsy in cerebral glioma: timing of appearance and histological correlations.

PURPOSE: A prospective study of patients with glioma was carried out. Special attention was paid to the first seizure, to the histology, and to the efficacy and prophylactic role of antiepileptic drugs (AEDs). Prognostic factors were analyzed. METHODS: Between February 1st 2004 and April 1st 2006 patients who underwent surgical treatment at the Neurosurgery Department of Bolzano for primary or recurrent glioma were prospectively followed until April 1st 2007. Seizures at onset occurring in the absence of interictal epileptiform abnormalities were regarded as remote symptomatic seizures and not treated. RESULTS: Sixty-four individuals were registered in the study; epilepsy diagnosis was made in 27 cases and in 24 of these seizures were the onset symptom. The correlation with histological grading showed that seizures were more frequent in low-grade glioma and in secondary glioblastoma. Although epilepsy was less frequent in high-grade glioma, in these patients seizures were more difficult to control. Poor seizure control was associated with motor and sensitive focal seizures and presence of neurological deficit. Patients without epilepsy and not taking AEDs never developed seizures during the follow-up. DISCUSSION: Our study clearly shows that epilepsy is more frequent in low-grade gliomas but seizures are more difficult to control in high-grade gliomas. In both cases seizures are a quite exclusive symptom at the onset that never appears during the stable course of the disease. Amongst glioblastoma multiforme (GBM), epilepsy is more frequent in GBM developing through progression from low-grade astrocitoma. Moreover, our study strongly indicates that the prophylactic use of AEDs in glioma is not justified.

Atypical meningioma of the sylvian fissure.

Meningiomas are meningothelial cell neoplasms that account for approximately 25% of all primary intracranial tumors. Most meningiomas are slow-growing benign lesions, and they are usually attached to the inner surface of the dura mater. Nevertheless, since the first description by of Cushing and Eisenhardt, many meningiomas without dural attachment have been reported. A subgroup located in the sylvian fissure (also called deep sylvian meningiomas) has been described, and these represent a radiological and neurosurgical challenge. We describe an atypical sylvian fissure meningioma in a 23-year-old male with a brief history of headache and mild hemiparesis. We also review the pertinent literature defining the major epidemiological, clinical, radiological and surgical characteristics of these rare tumors.

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.