Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces

Riechmann H. Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces. Bielefeld: Universität Bielefeld; 2014

Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces

Riechmann H. Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces. Bielefeld: Universität Bielefeld; 2014

Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces

Riechmann H, Finke A. Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces. In: ESANN 2012 proceedings. i6doc.com; 2012: 121-126.Non-stationarity is inherent in EEG data. We propose a concept for an adaptive brain computer interface (BCI) that adapts a classifier to the changes in EEG data. It combines labeled and unlabeled data acquired during normal operation of the system. The classifier is based on Fuzzy Neural Gas (FNG), a prototype-based classifier. Based on four data sets we show that retraining the classifier significantly increases classification accuracy. Our approach smoothly adapts to the session-to-session variations in the data

Hierarchical Codebook Visually Evoked Potentials for fast and flexible BCIs -- Dataset

Riechmann H. Hierarchical Codebook Visually Evoked Potentials for fast and flexible BCIs -- Dataset. Bielefeld University; 2014.This is the complete EEG dataset which was recorded for the linked study. A small README tells a bit about the data structure and the publication explains what data was recorded. For any further questions contact the author

Influence of Stimulus and Background Characteristics in cVEP-BCIs - Dataset

Riechmann H. Influence of Stimulus and Background Characteristics in cVEP-BCIs - Dataset. Bielefeld University; 2014

Using a cVEP-based Brain-Computer Interface to control a virtual agent -- dataset

Riechmann H. Using a cVEP-based Brain-Computer Interface to control a virtual agent -- dataset. Bielefeld University; 2014

Hierarchical Codebook Visually Evoked Potentials for fast and flexible BCIs

Riechmann H, Finke A, Ritter H. Hierarchical Codebook Visually Evoked Potentials for fast and flexible BCIs. In: 2013 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013). IEEE EMBS; 2013: 2776-2779

Asynchronous, parallel on-line classification of P300 and ERD for an efficient hybrid BCI

Riechmann H, Hachmeister N, Ritter H, Finke A. Asynchronous, parallel on-line classification of P300 and ERD for an efficient hybrid BCI. In: Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on. IEEE; 2011: 412-415

Using a cVEP-based Brain-Computer Interface to control a virtual agent

Riechmann H, Finke A, Ritter H. Using a cVEP-based Brain-Computer Interface to control a virtual agent. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2016;24(6):692-699

An Approach towards Human-Robot-Human Interaction Using a Hybrid Brain-Computer Interface

Hachmeister N, Riechmann H, Ritter H, Finke A. An Approach towards Human-Robot-Human Interaction Using a Hybrid Brain-Computer Interface. In: ICMI '11 Proceedings of the 13th International Conference on Multimodal Interaction. New York, NY, USA: ACM; 2011: 49-52