Automatic differentiation of multichannel EEG signals

Peters, Björn; Pfurtscheller, G.; Flyvbjerg, Henrik

doi:10.1109/10.900270

Cited by 121 publications

(67 citation statements)

References 17 publications

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“…Examples of spatial filters used in BCI development are the Laplace filter, Local Average Technique (LAT) and the Common Average Reference (CAR) (Peters et al, 2001). By definition, bipolar and mastoid referenced EEG data streams are also spatial filters since they produce an output by subtracting channels from a spatially distinct reference.…”

Section: Spatial Filteringmentioning

confidence: 99%

“…This represents an effort to reduce the noise content of the data by using noise samples from multiple channels. In (Peters et al, 2001), the performance of each filter for Artificial Neural Network classification of a 3-class intention of motion task is compared. The LAT filter performed worse than no filter.…”

Section: Spatial Filteringmentioning

confidence: 99%

“…The features of brain activity that are commonly used for Brain-Computer Interfacing are sensorimotor rhythms (Peters et al, 2001;Krusienski et al, 2007) slow cortical potentials (Birbaumber et al, 2003) and visually evoked responses (Lin et al, 2007;Citi et al, 2008). Visually evoked responses can either be oscillatory neuronal responses to repetitively delivered stimuli or delayed positive deflections in the EEG following the presentation of target stimuli.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

Syan¹

2010

American Journal of Applied Sciences

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Abstract:The P300 component of Event Related Brain Potentials (ERP) is commonly used in Brain Computer Interfaces (BCI) to translate the intentions of an individual into commands for external devices. The P300 response, however, resides in a signal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, attenuating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time consuming and delays the BCI response. Second, latency distortions may appear in the averaged result due to variable time-locking of the P300 in the individual trials. Problem statement: The use of single-trial P300 data overcomes both these shortcomings. However, single-trial data must be properly denoised to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-trial) as the data quality and recording circumstances are likely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II was used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in classifying unseen P300 spatiotemporal features in both the single-trial (74.19%) and multi-trial format (100%). It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general guide for practitioners developing single-trial and multi-trial P300-based BCI systems, particularly for selecting appropriate pre-processing agents and classification methodologies for inclusion. The possibilities for future study include the investigation of double-trial and triple-trial P300 system based on the LDA classifier. The time savings of such approaches will still be significant. It is very likely that such systems would benefit from accuracies higher than the one obtained in this study for single-trial LDA (74.19%).

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Section: Spatial Filteringmentioning

confidence: 99%

Section: Spatial Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

Syan¹

2010

American Journal of Applied Sciences

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“…Examples of such kernel-based classification methods are support vector machines (SVMs) (Vapnik, 1999) and kernel Fisher discriminant (KFD) . Better performances might also be achieved by using group (committee) of classifiers rather than using a single classifier but only a few BCI designs have employed such an approach in classifying features and achieved performance improvements Peters et al, 2001;Varsta et al, 2000;Millan et al, 2002).…”

Section: Main Classification Problems In Bci Researchmentioning

confidence: 99%

Signal Processing and Classification Approaches for Brain-Computer Interface

Al-Ani¹,

Trad²

2010

Intelligent and Biosensors

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“…Similar to the movement, there is the event-related (de)synchronization (ERD/ERS) and a slow activity denoted the readiness potential. Several studies have focused in distinguishing between the anticipation of movement of limbs [6], [7], [8], [9] in time or frequency domains. An important point of these last works that report decoding of anticipatory motor rhythms is that they are not self-paced and the subject is always cued.…”

mentioning

confidence: 99%

Classification of resting, anticipation and movement states in self-initiated arm movements for EEG brain computer interfaces

Rodrigo¹,

Montesano²,

Mínguez³

2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-In the last years, there has been an increasing interest in using Brain Computer Interfaces (BCI) within motor rehabilitation therapies that use robotic devices or functional electro stimulation to help or guide the efforts of the patient to move her body. A crucial step of these therapies is to provide help to the user just when she is actually trying to accomplish a certain motion or task One of the most promising applications of BCI systems in this context is its ability to measure the user intentions and actions to trigger the rehabilitation devices accordingly. This paper studies the single-trial classification based on EEG measurements of three basic states during the execution of self-initiated motion: rest, motion preparation (or anticipation) and motion. We conducted an experiment where the participants had to reach at their will eight different locations from a fixed starting position. Results for seven healthy subjects show that it is possible to achieve good classification rates given that features are carefully selected for each subject and for each pair of states.

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Automatic differentiation of multichannel EEG signals

Cited by 121 publications

References 17 publications

Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

Signal Processing and Classification Approaches for Brain-Computer Interface

Classification of resting, anticipation and movement states in self-initiated arm movements for EEG brain computer interfaces

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