A note on the Berlin Brain-Computer Interface

Müller, Klaus Robert; Krauledat, Matthias; Dornhege, Guido; Jähnichen, Stefan; Curio, Gabriel; Blankertz, Benjamin

doi:10.1007/1-4020-4043-1_6

Cited by 9 publications

(9 citation statements)

References 20 publications

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“…The GW6 method, as well as similar ones based on the correlation of the EEG signals (e.g. Radin, 2017), seems suitable for this purpose, but we believe more refined tools are necessary, for example those based on machine learning algorithms (Müller et al, 2008;Stober, Sternin, Owen, & Grahn, 2016), especially if we wish to detect signals after only a single stimulation and not, as we have done until now, after multiple repetitions of the same stimulation.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Brain-to-Brain interaction at a distance: a global or differential relationship?

Giroldini

Pederzoli

Bilucaglia

et al. 2017

Preprint

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Backgound: The main objective of this exploratory study was a confirmation of the results obtained by Giroldini et al, 2016, relative to the possibility of identifying a long-distance connection between the EEG activities of two totally isolated subjects, one of whom was stimulated with light and sounds. In this new study we have used the method of the steady-state stimulus (visual and auditory) given at the frequencies of 10, 12 and 14 Hz in order to answer the following questions:- What is the relationship between the power of the EEG response in the stimulated partner and that of the other isolated partner?- Is the relationship between the EEG activities of the stimulated and the isolated partner global (i.e., an undifferentiated response), or is it differentiated and thus displays variations depending on the characteristics of the stimulation applied to the stimulated partner?Methods: Five adults chosen for their experience in mind control techniques and their mutual friendships took part in this study. Each participant took turns in being both the stimulated partner and the isolated non-stimulated partner with each of the others, making a total of 20 pair combinations.The stimulated partner received three blocks of 32 visual-auditory stimulations lasting 1 second modulated at 10 Hz, 12 Hz, and 14 Hz respectively, with a constant inter-stimulus interval of 4 seconds.The EEG activity of each pair was recorded at 128 samples/sec over 14 channels and analyzed by measuring traditional steady-state potentials and the Pearson’s linear correlation between all possible signal pairs with an innovative algorithm.Results: From the results of twenty pairs of subjects, we found an overall increase in the correlation among the EEG channels of the isolated partners, therefore confirming the previous research.Furthermore, we did not find any correlation between the correlation strength among the EEG channels of the stimulated partner and that observed in the non-stimulated partner, suggesting that this physical characteristic cannot be transferred between isolated partners; but we did find that the correlation among the EEG channels of the isolated distant partners changed not only globally, but also when the frequencies perceived by the stimulated partners were outside the Alpha band, suggesting that this neurophysiological mental connection at a distance may be differentiated.

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Section: Discussionmentioning

confidence: 99%

Exploring the Brain-to-Brain interaction at a distance: a global or differential relationship?

Giroldini

Pederzoli

Bilucaglia

et al. 2017

Preprint

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“…Radin, 2017), seems suitable for this purpose, but we believe more refined tools are necessary, for example those based on machine learning algorithms (Müller et al, 2008;Stober, Sternin, Owen, & Grahn, 2016), especially if we wish to detect signals after only a single stimulation and not, as we have From a theoretical point of view, it seems fair to say that a mental connection at a distance can allow the transmission of some informational, rather than physical, characteristics of the shared signals. This seems plausible, given that we cannot theorize about any information transmission based on conventional electromagnetic waves.…”

Section: Discussionmentioning

confidence: 99%

Brain-to-Brain Interaction at a Distance: A Global or Differential Relationship?

et al. 2017

View full text Add to dashboard Cite

Backgound: The main objective of this exploratory study was a confirmation of the results obtained by , relative to the possibility of identifying a long-distance connection between the EEG activities of two totally isolated subjects, one of whom was stimulated with light and sounds.In this new study we have used the method of the steady-state stimulus (visual and auditory) given at the frequencies of 10, 12 and 14 Hz in order to answer the following questions:-What is the relationship between the power of the EEG response in the stimulated partner and that of the other isolated partner? -Is the relationship between the EEG activities of the stimulated and the isolated partner global (i.e., an undifferentiated response), or is it differentiated and thus displays variations depending on the characteristics of the stimulation applied to the stimulated partner? Results: From the results of twenty pairs of subjects, we found an overall increase in the correlation among the EEG channels of the isolated partners, therefore confirming the previous research.Furthermore, we did not find any correlation between the correlation strength among the EEG channels of the stimulated partner and that observed in the non-stimulated partner, suggesting that this physical characteristic cannot be transferred between isolated partners; but we did find that the correlation among the EEG channels of the isolated distant partners changed not only globally, but also when the frequencies perceived by the stimulated partners were outside the Alpha band, suggesting that this neurophysiological mental connection at a distance may be differentiated.

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“…The effectiveness of the proposed machine learning-based BCI system will be evaluated by utilizing one publicly accessible data set IVa of BCI Competition III [ 19 ], which is based on MI and small training sets. The dataset used in this study is not proprietary, instead, it is a publicly available dataset.…”

Section: Methodsmentioning

confidence: 99%

“…The dataset IVa for the BCI competition III encompasses five healthy volunteers (aa, al, av, aw, and ay). The data collection process mainly consists of the following steps: (1) volunteers were settled into a cozy chair, (2) using 118 channels, the EEG data from the five participants were gathered, (3) subjects were instructed to perform MI tasks such as right-hand movement (RH) and foot movement (F) during the EEG recording, (4) each individual was given a specific MI task to complete for 3.5 s, which was communicated by visual cues resulted in a total of 280 signs, and, (5) between each next visual sign, individuals were given brief intervals of about 2 s. The experimental design of each trial is described in Figure 1 [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Hashem

AbdulAzeem

Labib

et al. 2023

Sensors

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Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model’s predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

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A note on the Berlin Brain-Computer Interface

Cited by 9 publications

References 20 publications

Exploring the Brain-to-Brain interaction at a distance: a global or differential relationship?

Exploring the Brain-to-Brain interaction at a distance: a global or differential relationship?

Brain-to-Brain Interaction at a Distance: A Global or Differential Relationship?

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

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