A Survey on EEG Data Analysis Software

Das, Rupak Kumar; Martin, Anna; Zurales, Tom; Dowling, Dale; Khan, Arshia

doi:10.3390/sci5020023

Cited by 6 publications

(4 citation statements)

References 61 publications

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“…The raw EEG data are analyzed and processed to generate a topographical map, which displays the distribution of the electrical activity across the scalp. There are different possible techniques to generate heat map topography of the brain activity, whether by self-generated codes [59,60] or by using different software [61]. The brain activity illustration, represented in Figure 3b, displays the topographical representation or thermal brain mapping, using the six optimum electrodes for the four tested frequency bands.…”

Section: Eeg Topographic Mapsmentioning

confidence: 99%

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

et al. 2023

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Emotion recognition is a vital part of human functioning. textcolorredIt enables individuals to respond suitably to environmental events and develop self-awareness. The fast-paced developments in brain–computer interfacing (BCI) technology necessitate that intelligent machines of the future be able to digitize and recognize human emotions. To achieve this, both humans and machines have relied on facial expressions, in addition to other visual cues. While facial expressions are effective in recognizing emotions, they can be artificially replicated and require constant monitoring. In recent years, the use of Electroencephalography (EEG) signals has become a popular method for emotion recognition, thanks to advances in deep learning and machine learning techniques. EEG-based systems for recognizing emotions involve measuring electrical activity in the brain of a subject who is exposed to emotional stimuli such as images, sounds, or videos. Machine learning algorithms are then used to extract features from the electrical activity data that correspond to specific emotional states. The quality of the extracted EEG signal is crucial, as it affects the overall complexity of the system and the accuracy of the machine learning algorithm. This article presents an approach to improve the accuracy of EEG-based emotion recognition systems while reducing their complexity. The approach involves optimizing the number of EEG channels, their placement on the human scalp, and the target frequency band of the measured signal to maximize the difference between high and low arousal levels. The optimization method, called the simplicial homology global optimization (SHGO), is used for this purpose. Experimental results demonstrate that a six-electrode configuration optimally placed can achieve a better level of accuracy than a 14-electrode configuration, resulting in an over 60% reduction in complexity in terms of the number of electrodes. This method demonstrates promising results in improving the efficiency and accuracy of EEG-based emotion recognition systems, which could have implications for various fields, including healthcare, psychology, and human–computer interfacing.

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Section: Eeg Topographic Mapsmentioning

confidence: 99%

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

et al. 2023

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“…Frequency domain analysis is a widely used method for EEG analysis, and it is often conducted as the first analysis since it is a general and the most common method to understand the EEG data as a whole [57,58]. Frequency domain analysis is regarded as the most powerful and standard method for EEG analysis, compared to other methods The remaining epochs were analysed in the time domain and frequency domain.…”

Section: Frequency Domain Analysismentioning

confidence: 99%

Frequency and Time Domain Analysis of EEG Based Auditory Evoked Potentials to Detect Binaural Hearing in Noise

Ignatious

Azam

Jonkman

et al. 2023

JCM

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Hearing loss is a prevalent health issue that affects individuals worldwide. Binaural hearing refers to the ability to integrate information received simultaneously from both ears, allowing individuals to identify, locate, and separate sound sources. Auditory evoked potentials (AEPs) refer to the electrical responses that are generated within any part of the auditory system in response to auditory stimuli presented externally. Electroencephalography (EEG) is a non-invasive technology used for the monitoring of AEPs. This research aims to investigate the use of audiometric EEGs as an objective method to detect specific features of binaural hearing with frequency and time domain analysis techniques. Thirty-five subjects with normal hearing and a mean age of 27.35 participated in the research. The stimuli used in the current study were designed to investigate the impact of binaural phase shifts of the auditory stimuli in the presence of noise. The frequency domain and time domain analyses provided statistically significant and promising novel findings. The study utilized Blackman windowed 18 ms and 48 ms pure tones as stimuli, embedded in noise maskers, of frequencies 125 Hz, 250 Hz, 500 Hz, 750 Hz, 1000 Hz in homophasic (the same phase in both ears) and antiphasic (180-degree phase difference between the two ears) conditions. The study focuses on the effect of phase reversal of auditory stimuli in noise of the middle latency response (MLR) and late latency response (LLR) regions of the AEPs. The frequency domain analysis revealed a significant difference in the frequency bands of 20 to 25 Hz and 25 to 30 Hz when elicited by antiphasic and homophasic stimuli of 500 Hz for MLRs and 500 Hz and 250 Hz for LLRs. The time domain analysis identified the Na peak of the MLR for 500 Hz, the N1 peak of the LLR for 500 Hz stimuli and the P300 peak of the LLR for 250 Hz as significant potential markers in detecting binaural processing in the brain.

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“…Various established software packages exist for visualization, preprocessing, and analysis of EEG data (24). Leading general-purpose EEG software packages include open-source MATLAB-based Brainstorm (25), EEGLAB (26) and FieldTrip (27), and Python-based MNE-Python (28), all of which are continuously developed and are witnessing a rapidly expanding interest in the scientific community (fig.…”

Section: Introductionmentioning

confidence: 99%

“…Next, compute PSD per sleep stage using the average reference, 2048 samples length of the FFT, and the Hamming window with an overlap of 1024 samples (l. 11-17). Plot scalp topography distribution of PSDs per sleep stage and PSD plot (l [18][19][20][21][22][23][24]…”

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confidence: 99%

SleepEEGpy: a Python-based software “wrapper” package to organize preprocessing, analysis, and visualization of sleep EEG data

Belonosov,

Falach,

Schmidig

et al. 2023

Preprint

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Sleep research uses electroencephalography (EEG) to infer brain activity in health and disease. Beyond standard sleep scoring, there is increased interest in advanced EEG analysis that investigates activities at specific times, frequencies, and scalp locations. Such analysis requires preprocessing to improve the signal-to-noise ratio, and dedicated analysis algorithms and visualizations. While many EEG software packages exist, sleep research has specific considerations (e.g., avoiding particular artifacts, analysis, and visualization in an ongoing non-epoch nature, detection of characteristic oscillatory events, and interface with sleep staging) that require dedicated tools. Currently, sleep investigators typically use available libraries for specific tasks in a ‘fragmented’ configuration that is inefficient, prone to errors, and requires the burdensome learning of multiple software environments. Here, we present SleepEEGpy, an open-source Python package for sleep EEG data preprocessing and analysis, including (i) cleaning, (ii) independent component analysis, (iii) analysis of sleep events, (iv) analysis of spectral features, and associated visualization tools. SleepEEGpy builds upon MNE-Python, YASA, and SpecParam (formerly FOOOF) tools to provide an all-in-one package for comprehensive yet straightforward sleep EEG research. We demonstrate the SleepEEGpy pipeline and its functionalities by applying it to overnight high-density EEG data in healthy participants, revealing multiple characteristic activity signatures typical of each vigilance state. These include alpha oscillations in wakefulness, sleep spindle and slow wave activities in NREM sleep, and theta activity in REM sleep. We hope that this package will be embraced and further developed by the sleep research community, allowing investigators to focus more on science and improve reproducibility among research groups.

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A Survey on EEG Data Analysis Software

Cited by 6 publications

References 61 publications

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

Frequency and Time Domain Analysis of EEG Based Auditory Evoked Potentials to Detect Binaural Hearing in Noise

SleepEEGpy: a Python-based software “wrapper” package to organize preprocessing, analysis, and visualization of sleep EEG data

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