Journal of Neuroscience Methods

Kinematic analyses of rodent behavior are frequently used in neuroscience research, and commonly in spinal cord injury (SCI) studies. Unfortunately, skin motion artifact introduces significant errors into these data, because the skin is only loosely coupled to the underlying skeleton by connective tissue. In rats, these errors can be as large as 50-75%,as quantified by past work using x-ray fluoroscopy. Here we show that infrared videography of a subcutaneous tattoo can overcome skin motion artifact in rodent kinematics. The method yields data similar to gold standard x-ray fluoroscopy systems at a fraction of the cost, does not affect the animals locomotion, and results in markers that persist for at least 10 weeks. We found that, compared to a gold-standard x-ray fluoroscopy study that directly tracked the skeleton, our method reduced the error in mean hip angle from 17 {+/-} 6.0 to 3.1 {+/-} 2.4 degrees (mean {+/-} SEM), and the root-mean-square (RMS) error across the mean hip angle waveform from 20 to 5.3 degrees (n=4 rats). The knee joint angle waveform derived from infra-red imaging tightly matched the shape of the x-ray waveform after allowing for a constant offset, having RMS error reduced from 8.1 to 1.2 degrees. The method stands to significantly reduce between-animal errors, and hence between laboratory errors, in these ubiquitous model systems, especially important in SCI studies where individuals are assigned to different treatments.

ObjectiveElectroencephalography (EEG) studies increasingly make use of more ecologically valid experimental protocols involving mobile participants who actively engage with their environment (MoBI; Gramann et al., 2011). These mobile paradigms lead to increased artifacts in the recorded data that are often treated using Independent Component Analysis (ICA). When analyzing EEG data, especially in a mobile context, removing samples regarded as artifactual is a common approach before computing ICA. Automatic tools for this exist, such as the automatic sample rejection of the AMICA algorithm (Palmer et al., 2011), but the impact of the two factors movement intensity and the automatic sample rejection has not been systematically evaluated yet. ApproachWe computed AMICA decompositions on eight datasets from six open-access studies with varying degrees of movement intensities using increasingly conservative sample rejection criteria. We evaluated the subsequent decomposition quality in terms of the component mutual information, the amount of brain, muscle, and "other" components, the residual variance of the brain components, and an exemplary signal-to-noise ratio. Main resultsWe found that increasing movements of participants led to decreasing decomposition quality for individual datasets but not as a general trend across all movement intensities. The cleaning strength had less impact on decomposition results than anticipated, and moderate cleaning of the data resulted in the best decompositions. SignificanceOur results indicate that the AMICA algorithm is very robust even with limited data cleaning. Moderate amounts of cleaning such as 5 to 10 iterations of the AMICA sample rejection with 3 standard deviations as the threshold will likely improve the decomposition of most datasets, irrespective of the movement intensity.

Currently, implantation of electroencephalogram (EEG) electrodes in laboratory animals is time-consuming and requires specialized equipment. We present a novel method for EEG recordings in mice that utilizes thin needle electrodes. These electrodes are inserted into the skull at predetermined locations by gently pressing them against the bone surface. To ensure stable fixation of the implant, hook-shaped needles are positioned along the lateral aspects of the skull. The electrodes are connected to a multipin connector and secured to the skull using dental composite, after which the animal is allowed to recover from anesthesia. Importantly, procedures such as skull drilling and screw placement are not required, allowing the entire surgery to be completed in less than 15 minutes. Consequently, this EEG implantation approach is rapid and minimally invasive. Results of our studies indicate that EEG recordings obtained with needle electrodes are not inferior to those obtained with screw electrodes. Overall, the method is designed to enhance the accuracy and efficiency of EEG recording studies while improving animal welfare. O_LISimplifies the placement of EEG electrodes. C_LIO_LIReduces the time required for electrode implantation. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/715731v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@e5608org.highwire.dtl.DTLVardef@1325ea4org.highwire.dtl.DTLVardef@1e37202org.highwire.dtl.DTLVardef@1521bb8_HPS_FORMAT_FIGEXP M_FIG C_FIG

Determining the localization of intracerebral implants in rodent brain stands as a critical final step in most physiological and behaviroral studies, especially when targeting deep brain nuclei. Conventional histological approaches, reliant on manual estimation through sectioning and slice examination, are error-prone, potentially complicating data interpretation. Leveraging recent advances in tissue-clearing techniques and light-sheet fluorescence microscopy, we introduce a method enabling virtual brain slicing in any orientation, offering precise implant localization without the limitations of traditional tissue sectioning. To illustrate the methods utility, we present findings from the implantation of linear silicon probes into the midbrain interpeduncular nucleus (IPN) of anesthetized transgenic mice expressing chanelrhodopsin-2 and enhanced yellow fluorescent protein under the choline acetyltransferase (ChAT) promoter/enhancer regions (ChAT-Chr2-EYFP mice). Utilizing a fluorescent dye applied to the electrode surface, we visualized both the targeted area and the precise localization, enabling enhanced inter-subject comparisons. Three dimensional (3D) brain renderings, presented effortlessly in video format across various orientations, showcase the versatility of this approach.

BackgroundIn neurophysiological pain studies, multiple types of calibration methods are used to quantify the individual pain sensation stimuli that have different modalities. However, such studies often lack calibration procedure implementation, have a vague protocol description, do not provide data quality quantification, or even omit required control for gender pain differences. All this hampers not only study repetition but also interexperimental comparisons. Moreover, typical calibration procedures are long and require a high number of stimulations which may cause participants discomfort and stimuli habituation. MethodTo overcome those shortcomings, we present an automatic staircase pain calibration method for A-delta-specific electrical stimulation adjusted to the magnetoencephalography environment. We provide an in-depth data analysis of the collected self-reports from seventy healthy volunteers (37 males) and propose a method based on a dynamic truncated linear regression model (tLRM). We compare its estimates for the sensation (t), and pain (T) thresholds, as well as for the mid-pain stimulation (MP), with those calculated using a traditional threshold method and standard linear regression models. ResultsCompared to the other threshold methods, tLRM exhibits higher R2 and requires 36% fewer stimuli application and has significantly higher t and lower T and MP intensities. Regarding sex differences, both lower t and T were found for females compared to males, regardless of the calibration method. ConclusionsThe proposed tLRM method quantifies the quality of the calibration procedure, minimizes its duration and invasiveness, as well as provides validation of linearity between stimuli intensity and subjective scores, making it an enabling technique for further studies. Moreover, our results highlight the importance of control for gender in pain studies. SummaryThe purpose of this study was to shorten and automatize the calibration method which is an enabling technique for realizing neurophysiological studies on pain. The proposed method is based on a dynamic truncated linear regression model and was shown to require 36% fewer stimuli application compared to the traditional staircase method. Furthermore, the calibration was adjusted to A-delta specific intraepidermal electrical stimulation, quantifies the quality of the resulting calibration parameters and provides a validation of linearity between stimuli intensity and subjective scores. The results also highlight the importance of control for participant gender in studies where different types of stimulation are used to induce pain sensation.

Although commonly known as rapid and easy to use methodology, Golgi staining requires a range of staining solutions, impregnation periods, concentrations and slicing variables. The use of this methodology can help researchers identify and label individual neuronal components within the extended circuitry. The original Golgi stain technique, developed by Camillo Golgi in 1873, is a silver staining method that enabled scientists to visualize individual neurons in their entirety within nervous tissue for the first time. publications featuring the Golgi staining technique utilise cryostat or microtome slicing, with the combination of a readily purchased kit which comes with a cost and limited morphological detail. Here, we describe an optimised Golgi staining methodology that specifically targets the major drawbacks of traditional protocols; prolonged and inconsistent impregnation, slice fragility during sectioning, and variable visualization of fine dendritic structures. Through modest adjustments to impregnation duration and temperature, fixation, and vibratome sectioning conditions, this low-cost and simple protocol improves staining reliability, facilitates robust slicing without specialized embedding, and supports detailed analysis of neuronal morphology throughout the central nervous system. We validate our optimised protocol using tissue from on-going animal studies of pain and treatment. Representative images illustrate typical staining patterns, characterised by sparse background and high signal-to-noise ratio, facilitating unbiased neuronal tracing and analysis.

Investigating the neurophysiology of nociception is aided by electrophysiological recording from dorsal root ganglion (DRG) neurons. Because DRG neurons are heterogeneous with overlapping electrophysiological properties, methods to distinguish neuron subtypes are valuable for properly interpreting the measurements and drawing conclusions. Automated patch clamp recording offers an approach for conducting these experiments at higher throughput than conventional recording methods, but identification of neuron subtypes is challenging. We developed a method for recording from acutely isolated mouse DRG neurons using automated patch clamp recording coupled to optogenetic stimulation that was capable of discerning NaV1.8 and TRPV1 expressing neuron subpopulations. This approach can facilitate physiological and pharmacological studies of DRG neurons with potential value in developing and testing targeted analgesic agents.

PyRodentTracks (PRT) is a scalable and customizable computer vision and RFID- based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PRT is composed of the online Raspberry Pi-based video and RFID acquisition and the subsequent offline analysis tools. The system is capable of tracking up to 6 mice in experiments ranging from minutes to days. PRT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared to manual validation of videos containing 1-4 mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. Moreover, it was observed that novel non-cagemate mice pairs exhibit more similarity in travel trajectory patterns over a 10-minute period in the openfield than cagemates. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PRT can interface with open-source packages such as Deeplabcut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PRT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable rodent-specific behavior recording and analysis system. Statement of SignificanceAn affordable, customizable, and easy-to-use open-source rodent tracking system is described. To tackle the increasingly complex questions in neuroscience, researchers need a flexible system to track rodents of different coat colors in various complex experimental paradigms. The majority of current tools, commercial or otherwise, can only be fully automated to track multiple animals of the same type in a single defined environment and are not easily setup within custom arenas or cages. Moreover, many tools are not only expensive but are also difficult to set up and use, often requiring users to have extensive hardware and software knowledge. In contrast, PRT is easy to install and can be adapted to track rodents of any coat color in any user-defined environment with few restrictions. We believe that PRT will be an invaluable tool for researchers that are quantifying behavior in identified animals.

Electroencephalography (EEG) data are inherently contaminated by non-neuronal noise, including eye movements, muscle activity, cardiac signals, electrical interference, and technical issues such as poorly connected electrodes. Preprocessing to remove these artefacts is essential, yet the optimal method remains unclear due to the vast number of available techniques, their combinatorial use in pipelines, and adjustable parameters. Consequently, most studies adopt ad hoc preprocessing strategies based on dataset characteristics, study goals, and researcher expertise, with little justification for their choices. Such variability can influence downstream results, potentially determining whether effects are detected, and introduces risks of questionable analytical practices. Here, we present a method to objectively evaluate and compare preprocessing pipelines. Our approach uses realistically simulated signals injected into real EEG data as "ground truth", enabling the assessment of a pipelines ability to remove noise without distorting neuronal signals. This evaluation is independent of the studys main analyses, ensuring that pipeline selection does not bias results. By applying this procedure, researchers can select preprocessing strategies that maximize signal-to-noise ratio while maintaining the integrity of the neural signal, improving both reproducibility and interpretability of EEG studies. Although the data presented here focuses on processing and analysis most relevant for ERP research, the method can be flexibly expanded to other types of analyses or signals.

In recent years, an increasing number of large electrophysiological data sets have become publicly available, thereby providing researchers with the opportunity to analyze spike train data without conducting their own experiments. While this is undoubtedly a positive development, it increases the need for proper documentation on how the data were collected and what preprocessing was performed on the data, since interpreting analysis results in ignorance of these pieces of information can lead to wrong conclusions. An important preprocessing step is the removal of artifacts from the recordings. Electrophysiological recordings are particularly susceptible to electrical cross-talks between recording channels, resulting in artifact spikes that are coincident in multiple channels on the time scale of the data sampling rate, i.e., 1/30 ms in popular setups. The removal by signal whitening is only possible if also the raw sampled data are available, thus to eliminate this type of artifact is to remove all coincident spikes on the recording time scale to definitely avoid artifact spikes. However, given the lack of the "ground truth", this step has the potential to eliminate, in conjunction with the artifacts, components of the data that are pertinent to the research objective. In this study, we use a modified version of the Unitary Event Analysis and demonstrate that such preprocessing results in significantly lower correlations than expected by chance even on longer time scales. We also propose a method to correct for the bias introduced by this preprocessing. Thus, slight changes in the preprocessing have potentially strong impact on analysis results and methods need to consider these effects.

ObjectiveThe acquisition of electroencephalogram (EEG) data during neurostimulation, particularly concurrent transcutaneous electrical stimulation of the auricular vagus nerve, introduces unique challenges for data preprocessing and analysis due to the presence of significant stimulation artifacts. This study evaluates various denoising techniques to address these challenges effectively. MethodsA variety of denoising techniques were investigated, including interpolation methods, spectral filtering, and spatial filtering techniques. The techniques evaluated included low-pass and notch filtering, spectrum interpolation, average artifact subtraction, the Zapline algorithm, and advanced methods such as independent component analysis (ICA), signal-space projection (SSP), and generalized eigendecomposition with stimulation artifact source separation (GED/SASS). The efficacy of these algorithms was evaluated across three distinct datasets: simulated data, data from a gelatin phantom model, and real human subject data. ResultsOur findings indicate that GED (SASS) and SSP significantly outperformed other methods in reducing artifacts while preserving the integrity of the EEG signal. ICA and Zapline were effective too, but came with important limitations. These methods demonstrated robustness across different data types and conditions, providing effective artifact mitigation with minimal disruption to other essential signal components. ConclusionThis comprehensive analysis demonstrates the efficacy of advanced spatial filtering techniques in the preprocessing of EEG data during auricular vagus nerve stimulation. These methods offer promising avenues for enhancing the quality and reliability of neurostimulation-associated EEG data, facilitating a deeper understanding and wider applications in clinical and research settings.

Longitudinal brain imaging is essential for understanding neural mechanisms. Here, we present a saline-free, chronic preparation for repeated neural recording in adult Drosophila over multiple days. We describe steps for mounting flies, performing manual surgery on the head cuticle without external saline, and resealing the opening to create a transparent optical window. We demonstrate the utility of this approach by tracking single-neuron spiking and neuronal calcium dynamics over 7-10 days. This protocol is potentially applicable to other insect species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/706199v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@abeb34org.highwire.dtl.DTLVardef@deaf93org.highwire.dtl.DTLVardef@1d8fc24org.highwire.dtl.DTLVardef@91a696_HPS_FORMAT_FIGEXP M_FIG C_FIG

Electroencephalogram (EEG) signals are a valuable recording technique to diagnose neurological disorders and identify noninvasive biomarkers for clinical application, however, they are vulnerable to various artifacts. It is difficult to define exact parameters which efficiently distinguish artifacts from neural activity, and thus cleaning EEG data often relies on labor-intensive visual scoring methods. While signal processing techniques to remove artifacts exist, many state-of- the-art techniques are designed for multivariate signals, which can be challenging to implement in recording setups with few electrodes. We demonstrate how the spectral slope - a method previously used to distinguish between conscious states by linear regression of the logarithmic EEG power spectra - can also be used to identify epochs contaminated by recording artifacts in rat EEG recordings and propose this as a first pass artifact detection method. We computed the mean spectral slope for both clean and noisy epochs and compared the distributions among individual recordings to determine whether the decision threshold should be dynamic or fixed. We found no significant difference between the mean of these distributions and determined that a spectral slope threshold of -8 V 2/Hz was effective at identifying noisy epochs across all recordings. The accuracy of our method was evaluated against visually scored recordings and obtained an average accuracy, F1 and Cohen Kappa score of 94.2%, 86.4%, and 83%, respectively, across all epochs. Our study contributes to the automation of EEG artifact detection by presenting a straightforward initial method for identifying contaminated epochs based on the spectral slope of a single EEG channel in rodent recordings.

With the recent development and rapidly accelerating adoption of machine-learning based rodent behavioral tracking tools such as DeepLabCut, there is an unmet need for a method of acquiring video data that is scalable, flexible, and affordable. Many experimenters use webcams, GoPros, or other commercially available cameras that are not only relatively expensive, but offer very little flexibility over recording parameters. These cameras are not ideal for recording many types of behavioral experiments, and can lead to suboptimal video quality. Furthermore when using relatively affordable commercially available products, it is a challenge, if not impossible, to synchronize multiple cameras with each other, or to interface with third-party equipment (for example, receiving a simple trigger to simultaneously start recording, or acting as a microcontroller for closed-loop experiments). We have developed an affordable ecosystem of behavioral recording equipment, PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture), that relies on Raspberry Pi Camera Boards that are able to acquire high quality recordings in bright light, low light, or dark conditions under infrared light. PiRATeMC offers users control over nearly every recording parameter, and can be fine-tuned to produce optimal video data in any behavioral arena. This setup can easily be scaled up and synchronously controlled in clusters via a self-contained network to record a large number of simultaneous behavioral sessions without burdening institutional network infrastructure. Furthermore, the Raspberry Pi is an excellent platform for novice and inexperienced programmers interested in using an open-source recording system, with a large online community that is very active in developing novel open-source tools. It easily interfaces with Arduinos and other microcontrollers, allowing simple synchronization and interfacing of video recording with nearly any behavioral equipment using GPIO pins to send or receive 3.3V or 5V (TTL) signals, I2C, or serial communication.

1Responder analyses for the evaluation of randomized clinical trial (RCT) data have become more common in the recent past, since they can provide the medical community with results that are more directly applicable to clinical care. For pain studies, the predominant responder analysis compares the change in the individual participants pain level at baseline to their value at the end of the study period and uses a predetermined clinically important change cut-off value to define a response. While useful, this method substantially reduces the efficiency of the RCT by dichotomizing the results and is limited to comparing the baseline to the end of the study only. In this paper, we introduce a novel approach to the patient response over time with a focus on single dose post-operative studies. This technique provides graphical presentations and statistical approaches to understand the onset of any specified level of response, the maximum proportion of patients with a response at any point in time, and the duration of that response over time. In addition, each outcome can be summarized to examine the result across all possible cut-off points for clinically important differences (CID). We accomplish this by introducing three interrelated, longitudinal efficacy statistics: ROOT, GRO, and GROOT. The response outcome over time (ROOT) estimates the total proportion of a study period an individual patient spends as a responder. The group response outcome (GRO) estimates the instantaneous proportion of responders at all time points across the study period. The group response outcome over time (GROOT) summarizes total efficacy in a cohort, and can be calculated as the area under the GRO curve, or as the mean ROOT; they are identical. This novel method provides a clinically interpretable responder analysis over the full period of the study and, by using every data point across time, mitigates the loss of statistical power typically associated with dichotomized responder outcomes. Group response analysis is based upon repeated assessments of categorical or continuous measures categorizing each participants status as a treatment responder or non-responder at every timepoint based on the prespecified clinically important difference. Both the visual and statistical comparison of any two or more curves provide a comparison of the overall efficacy, which can be statistically tested using a standard asymptotic hypothesis test (such as Wald (Johnson & Romer, 2016)). The method allows for an integrated evaluation of three main components of drug efficacy: the proportion of participants achieving a CID over time (effect), the time to achieve that response (onset), and the length of the response (duration). In this paper, we present the group response analysis methodology and then illustrate it using data from a placebo-controlled randomized clinical trial (RCT) for postoperative pain after third molar extraction treated with meloxicam and ibuprofen as an active comparator (Christensen et al., 2018). Our approach yields similar effect sizes as the sum of pain intensity differences (SPID) commonly used for pain study analyses while providing superior clinical interpretability and a more complete evaluation of drug therapies beyond just efficacy. We propose that this method can be used as a primary or secondary analysis of pain RCTs to answer the question of the patient response to treatment and provide suitable data to compare efficacies across treatment groups.

Adeno-associated viral (AAV) vectors allow for site-specific and time-dependent genetic manipulation of neurons. However, for successful implementation of AAV vectors, major consideration must be given to the selection of viral serotype and route of delivery for efficient gene transfer into the cell type being investigated. Here we compare the transduction pattern of neurons in the somatosensory system following injection of AAV9 or AAV2retro in the parabrachial complex of the midbrain, the spinal cord dorsal horn, the intrathecal space, and the colon. Transduction was evaluated based on Cre-dependent expression of tdTomato in transgenic reporter mice, following delivery of AAV9 or AAV2retro carrying identical constructs that drive the expression of Cre/GFP. The pattern of distribution of tdTomato expression indicated notable differences in the access of the two AAV serotypes to primary afferent neurons via peripheral delivery in the colon and to spinal projections neurons via intracranial delivery within the parabrachial complex. Additionally, our results highlight the superior sensitivity of detection of neuronal transduction based on reporter expression relative to expression of viral products.

ObjectiveWhen modeling task-related human brain activity it is often necessary to simulate brain signals with specific mutual correlations between them. The signals should resemble those observed in practice, and consist of an "evoked" ("phase-locked") component and a random oscillatory part. To be neurophysiologically plausible their waveforms must be shaped in a certain way or exhibit specific global features; in technical terms - they should be modulated by a certain envelope function. The goal of this technical note is to describe a simple way of how such signal sets can be obtained. MethodsWe derive a procedure which allows generating multi-epoch signals with the above properties. This is done by mixing a "seed" set of waveforms typically reflecting particular qualities of the target brain activity. As an example, the seed set can consist of realizations of colored noise with desired power spectrum, or can be obtained from real brain measurements. ResultsThe algorithm yields a set of n multi-epoch signals with specified mutual correlations. Evoked parts, oscillatory parts and global envelopes of the signals can be controlled independently in order to obtain desired properties of the generated time courses. ConclusionThe procedure provides versatile sets of mutually correlated signals suitable for modeling task-related brain activity. SignificanceIn contrast to other methods often relying on complicated computations, the suggested approach is straightforward and easy to apply in everyday practical work, yet yielding realistic "functionally connected" simulated brain signals.

Cell assemblies, i.e., concurrently active groups of neurons, likely underlie neural processing for higher brain functions. Recent technological progress has enabled large-scale recording of neuronal activity, permitting the exploration and analysis of cell assembly dynamics. This review aims to provide both conceptual insights and practical knowledge pertaining to principal methodologies used for detecting cell assemblies in the last fifteen years. The goal is to assist readers in selecting and comparing various protocols to optimize their data processing and analysis pipeline. Each algorithm is explained with its fundamental principles, their application in neuroscience for cell assembly detection, and illustrated with published studies. Recognizing the similarities, advantages, and drawbacks of diverse methodologies may pave the way for developing new procedures for cell assembly identification to facilitate future endeavors in the understanding of brain activity.

Experiments aiming to understand sensory-motor systems, cognition and behavior often require animals trained to perform complex tasks. Traditional training protocols require lab personnel to move the animals between home cages and training chambers, to start and end training sessions, and in some cases, to hand-control each training trial. Human labor not only limits the amount of training per day, but also introduces several sources of variability and may increase animal stress. Here we present an automated training system for the 5-choice serial reaction time task (5CSRTT), a classic rodent task often used to test sensory detection, sustained attention and impulsivity. We found that fully automated training without human intervention greatly increased the speed and efficiency of learning, and decreased stress as measured by corticosterone levels. Introducing training breaks did not cancel these beneficial effects of automated training, and mice readily generalized across training systems when transferred from automated to manual protocols. Additionally, we validated our automated training system with mice implanted with wireless optogenetic stimulators, expanding the breadth of experimental needs our system may fulfill. Our automated 5CSRTT system can serve as a prototype for fully automated behavioral training, with methods and principles transferrable to a range of rodent tasks.

ObjectElectroencephalography (EEG) signals suffer from a low signal-to-noise ratio and are very susceptible to muscular, ambient noise, and other artifacts. Many artifact removal algorithms have been proposed to address this problem. However, the evaluation of these algorithms is conventionally too indirect (e.g., black-box comparisons of brain-computer interface performance before and after removal) because it is unclear which part of the signal represents raw EEG and which is noise. This project objectively benchmarks popular artifact removal algorithms and evaluates the fundamental Independent Component Analysis (ICA) approach thanks to a unique dataset where EEG is recorded simultaneously with other physiological signals-facial electromyography (EMG), accelerometers, and gyroscope-while ten subjects perform several repetitions of common artifact-inflicting tasks (blinking, speaking, etc.). ApproachI have compared the correlation between EEG signals and the artifact-representing channels before and after applying an artifact removal algorithm across the different artifact-inflicting tasks. The extent to which an artifact removal method can reduce this correlation objectively quantifies its effectiveness for the different artifacts. In the same direction, I have determined to what extent ICA successfully detects artefactual components in EEG by comparing the corresponding correlations for independent components that are labelled as artifacts with those labeled as EEG. Main resultThe FORCe was found to be the most effective and generic artifact removal method, cleaning almost 40% of artifacts. ICA is shown to be able to isolate almost 70% of artefactual components. SignificanceThis work alleviates the problem of unreliable evaluation of EEG artifact removal frameworks and provides the first reliable benchmark for the most popular algorithms in this literature.