Brain vision analyzer

Manufactured by Brain Products

Sourced in Germany

The Brain Vision Analyzer is a professional-grade software suite designed for the analysis and visualization of electrophysiological data, including EEG, ERP, and other neurophysiological signals. It provides a comprehensive set of tools for data import, preprocessing, analysis, and reporting.

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Lab products found in correlation

113 protocols using brain vision analyzer

EEG Data Preprocessing and Analysis

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Participants were fitted for a standard 10–20 32-channel active electrode cap containing Ag/AgCl electrodes (Acticap, Brain Vision). The “active” electrodes contain noise subtraction circuits that significantly reduce electrical interference. A reference electrode was placed at a frontal-central midline site (FCz). Electrodes were filled with a non-toxic conductive gel in order to lower impedances, then electrodes were connected to an EEG signal amplifier and recording software (LiveAmp, Brain Vision, LLC). The impedance for each electrode was kept at or below 25 kΩ. Recordings were digitized at 500 Hz.
Electroencephalography (EEG) data preprocessing was conducted using the Brain Vision Analyzer software (BrainVision Analyzer (Version 2.2.0) (2019) , Brain Products GmbH, Gilching, Germany). Trials with incorrect responses (mean incorrect Simon: 1.82; mean incorrect Stroop: 10.34) were excluded from final averages, and a band-pass filter from 0.1 to 30 Hz was applied. No specific data reduction parameters were used. Channels were referenced to the average mastoids. The data was segmented from 100 ms before the stimulus onset to 800 ms afterward, with the 100 ms pre-stimulus interval serving as the baseline for correction. Data segments were visually inspected for artifacts, and eye blink correction was performed using independent component analysis (ICA).

Velasquez M.A., Winston J.L., Sur S., Yurgil K., Upman A.E., Wroblewski S.R., Huddle A, & Colombo P.J. (2024). Music training is related to late ERP modulation and enhanced performance during Simon task but not Stroop task. Frontiers in Human Neuroscience, 18, 1384179.

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EEG Power Spectrum Analysis Protocol

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In preparation of the power-spectrum analysis, the EEG was down-sampled to 256 Hz. An automatic artefact rejection function in Brain Vision Analyzer (Brain Products GmbH, Gilching, Germany) was applied additionally to reduce the possible threshold change upon voltage step gradient that were not corrected by the ICA. The power-spectrum analysis was performed via installed FFT in Brain Vision Analyzer (Brain Products GmbH, Gilching, Germany). The FFT converted 1-s time windows with Hanning window function and yielded a resolution of 0.5 Hz into four frequency bands: delta (1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz) and beta (13–30 Hz). The absolute powers were then averaged over all time windows for each frequency band and subsequently ln-transformed for further statistical analysis. We examined the absolute power at four regions by averaging the power at corresponding electrodes: frontal (F3, F4, Fz), central (C3, C4, Cz), parietal (P3, P4, Pz) and occipital (O1, O2).

Huang J., Ulke C, & Strauss M. (2019). Brain arousal regulation and depressive symptomatology in adults with attention-deficit/hyperactivity disorder (ADHD). BMC Neuroscience, 20, 43.

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Processing and Analysis of Auditory Evoked Responses

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After the recording, the responses were preprocessed with BrainVision Analyzer (2.0; Brain Products). First, responses were off‐line bandpass filtered from 80 to 3,500 Hz (12 dB/octave, zero phase‐shift; Aiken & Picton, 2008; Bidelman, Moreno, & Alain, 2013; Krishnan, 2002). Responses were then segmented into epochs of 310 ms (−40 ms before stimulus onset and 270 ms after stimulus onset). Time points were adjusted by 7 ms to account for the neural lag inherent in FFR. After baseline correcting each response to the mean voltage of the noise floor (−40 to 0 ms), trials with activity exceeding the range of ±35 μV were rejected. For each stimulus, at least 1,000 artifact‐free trials were obtained, discarding any additional trials that might have been collected (Skoe & Kraus, 2013).

Yi H.G., Xie Z., Reetzke R., Dimakis A.G, & Chandrasekaran B. (2017). Vowel decoding from single‐trial speech‐evoked electrophysiological responses: A feature‐based machine learning approach. Brain and Behavior, 7(6), e00665.

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Preprocessing EEG Data for MSE Analysis

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Offline, the EEG signals were filtered using IIR (zero phase shift) and Butterworth filters between 0.1 and 50 Hz (order = 2; time constant = 1.59 s) and recalculated to average reference using Brain Vision Analyzer (Brain Products, Germany). Further preprocessing steps were executed in EEGLAB (Delorme and Makeig, 2004 (link)); SASICA (EEGLAB plugin; Chaumon et al., 2015 (link)) was used to remove eye blinks, movement, and electro-cardiac artifacts. We applied SASICA on the basis of autocorrelation measures and focal topography. Noisy components like muscle movements tend to show low autocorrelation. Therefore, muscle artifacts were detected by measuring the time-point by time-point variability, which was captured by low autocorrelation measures. Tonic muscle artifacts were detected based on their noise patterns and focused topography on electrodes around the edge of the EEG cap. Since the time window for the MSE analyses was defined from the onset of the stimulus until the onset of the participant’s typing response (see Supplementary Figure S1), the probability of muscle movement artifacts during this interval was very low.

Kaur Y., Ouyang G., Sommer W., Weiss S., Zhou C, & Hildebrandt A. (2020). What Does Temporal Brain Signal Complexity Reveal About Verbal Creativity?. Frontiers in Behavioral Neuroscience, 14, 146.

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EEG Data Preprocessing and Analysis

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Brain Vision Analyzer (Brainproducts, Munich, Germany) software was used for EEG data preprocessing. Firstly, raw data digitally were filtered between 0.01 and 60 Hz using the IIR filters function (order: 8), and then, ICA (Independent Component Analysis) was applied to filtered data to remove eye blinking and eye movement-related components from the data. Later, continuous data were segmented into the 2 s epochs, which are 1,000 ms before stimulus onset and 1,000 ms after stimulus onset (−1,000 + 1,000 ms). The epochs which consisted of the correct responses only were included in the analysis. The remaining artifacts (muscle movement and electrode artifact etc.) were removed with the offline manual artifact rejection by carefully checking the data epoch by epoch. Finally, preprocessed EEG data was exported from Brain Vision Analyzer for time-frequency analyses.

Güntekin B., Alptekin S., Yıldırım E., Aktürk T., Uzunlar H., Çalışoğlu P., Ada F.E., Atay E, & Ceran Ö. (2023). Immature event-related alpha dynamics in children compared with the young adults during inhibition shown by day-night stroop task. Frontiers in Human Neuroscience, 17, 1218559.

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EEG Data Preprocessing for Sleep Analysis

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EEG data preprocessing was conducted using BrainVision Analyzer software (2.2; Brain Products, Gilching, Germany). Data were filtered using a high- (0.1 Hz) and low-pass (40 Hz) filter with an additional notch filter at 50 Hz and rereferenced to averaged mastoids. Next, data were segmented in 30 s epochs of NREM sleep based on sleep scoring results. Afterwards, data were further segmented into equally sized segments of 2048 data points (4 s, 102 points overlap). Next, an automatic artifact rejection was applied (Ackermann et al. 2015 (link)) based on the following 3 criteria: (1) the maximum difference in EMG activity < 150 μV, (2) maximum voltage step in all EEG channels < 50 μV/ms, (3) maximum difference in EEG activity < 500 μV in all EEG channels. The number of removed segments were manually checked. For analysis of oscillatory activity during sleep (power analysis, spindle detection, and slow-wave detection), artifact rejected data were exported as continuous data and further analyzed using the SleepTrip toolbox (https://www.sleeptrip.org/; RRID: SCR_017318), which is based on FieldTrip functions (http://fieldtriptoolbox.org; RRID: SCR_004849; Oostenveld et al. 2011 (link)) and the SpiSOP tool (www.spisop.org; RRID: SCR_015673).

Beck J., Loretz E, & Rasch B. (2022). Stress dynamically reduces sleep depth: temporal proximity to the stressor is crucial. Cerebral Cortex (New York, NY), 33(1), 96-113.

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Analyzing ECG Data for Physiological Stress

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ECG data were cut into the whole sleep time and 15 min segments and exported in EDF+ format using the BrainVision Analyzer software (2.2; Brain Products, Gilching, Germany). Data were further analyzed using Kubios HRV Premium 3.2.0 (Kubios Oy, Kuopio, Finnland). The software includes an automatic artifact correction based on successive RR peak intervals. Data were analyzed in the time and frequency domain and the following variables were included in our analysis: mean heart rate (as an index for physiological arousal (Kogler et al. 2015 (link))) and the activity of the parasympathetic (PNS-index, based on mean RR peak intervals) as well as sympathetic nervous system (SNS-index, based on mean heart rate) to assess physiological stress.

Beck J., Loretz E, & Rasch B. (2022). Stress dynamically reduces sleep depth: temporal proximity to the stressor is crucial. Cerebral Cortex (New York, NY), 33(1), 96-113.

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Assessing Conditioned Responses through SCR Analysis

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Raw SCR data were pre-processed with Brain Vision Analyzer software (Brain Products GmbH, Munich, Germany). All further analyses were conducted semi-automatically using MATLAB, version 9.6.0.1114505 (R2019a, The MathWorks Inc., Natrick, MA). SCR to CS presentation was defined as the maximum amplitude recorded within the time window starting 1 s after CS onset and ending 6.5 s after CS onset. The CR was quantified as the difference between the average SCR across CS+ trials and the average SCR across CS-trials (Figure 1, bottom right). SCR to US presentation was defined as the maximum amplitude recorded within the time window starting 6.5 s after CS onset and ending 12 s after CS onset.

Fraenz C., Metzen D., Merz C.J., Selpien H., Axmacher N., & Genç E. (2020). Conditioned fear reactions are associated with gray matter density but not cortical microstructure.

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EEG Artifact Removal and Analysis

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Blink artifacts were detected using ICA (64 EEG electrodes were included), and the component identified as a blink was removed using the linear derivation function in Brain Vision Analyzer. Epochs containing misses (no button press 150–1150 ms post-stimulus onset) and eye saccades were excluded from further analysis. EEG epochs with amplitude of more than 75 μV at any electrode were excluded (performed using Brain Vision Analyzer version 1.05, Brain Products GmbH, Germany).

Fogelson N., Litvak V., Peled A., Fernandez-del-Olmo M, & Friston K. (2014). The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding. Schizophrenia Research, 158(1-3), 204-212.

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EEG Data Analysis of Ball Reception

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EEG data were recorded from eight active Ag/AgCl electrodes positioned at Fp1, Fp2, AFz, F3, Fz, F4, Cz, and Pz. The analysis was focused on the midline electrodes (Fz, Cz, Pz), which were already identified to be highly sensitive to record the components of interest (Niedeggen et al., 2014) (link). Active electrodes (impedance < 5 kΩ) were referenced to linked earlobes. Vertical and horizontal electrooculogram (EOG) were recorded to for ocular artifacts. Biosignals were recorded continuously with EEG-8 amplifiers (Contact precision instruments, Cambridge, UK) at 500 Hz. Offline, EEG data were analyzed using Brain Vision Analyzer (Version 1.05, Brain Products GmbH, Gilching, Germany). EEG was segmented according to the onset of ball reception (-100 to 700 ms epoch length), filtered (0.3-30 Hz, 24 dB/ Oct), and baseline corrected (-100 to 0 ms). Single EEG sweeps containing muscular or ocular artifacts were excluded from analysis, as well as EEG trials with high alpha activity (>80 μV). Because there were more segments for ball possession in the condition inclusion (INC) by definition, the number of EEG segments was randomly chosen to adjust it to the number of segments obtained in the condition partial exclusion (pEXC).

Niedeggen M., Kerschreiter R., Hirte D, & Weschke S. (2017). Being low prepares for being neglected: Verticality affects expectancy of social participation. Psychonomic bulletin & review, 24(2).

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