bci.bib

@article{morgan1996selective,
  title={Selective attention to stimulus location modulates the steady-state visual evoked potential},
  author={Morgan, ST and Hansen, JC and Hillyard, SA},
  journal={Proceedings of the National Academy of Sciences},
  volume={93},
  number={10},
  pages={4770--4774},
  year={1996},
  publisher={National Academy of Sciences},
  abstract = {Steady-state visual evoked potentials (SSVEPs) were recorded from the scalp of human subjects who were cued to attend to a rapid sequence of alphanumeric characters presented to one visual half-field while ignoring a concurrent sequence of characters in the opposite half-field. These two-character sequences were each superimposed upon a small square background that was flickered at a rate of 8.6 Hz in one half-field and 12 Hz in the other half-field. The amplitude of the frequency-coded SSVEP elicited by either of the task-irrelevant flickering backgrounds was significantly enlarged when attention was focused upon the character sequence at the same location. This amplitude enhancement with attention was most prominent over occipital-temporal scalp areas of the right cerebral hemisphere regardless of the visual field of stimulation. These findings indicate that the SSVEP reflects an enhancement of neural responses to all stimuli that fall within the "spotlight" of spatial attention, whether or not the stimuli are task-relevant. Recordings of the SSVEP provide a new approach for studying the neural mechanisms and functional properties of selective attention to multi-element visual displays. }
}


@article{wolpaw2002brain,
  title={Brain--computer interfaces for communication and control},
  author={Wolpaw, Jonathan R and Birbaumer, Niels and McFarland, Dennis J and Pfurtscheller, Gert and Vaughan, Theresa M},
  journal={Clinical neurophysiology},
  volume={113},
  number={6},
  pages={767--791},
  year={2002},
  publisher={Elsevier}
}


@article{muller2005steady,
  title={Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.},
  author={M{\"u}ller-Putz, Gernot R and Scherer, Reinhold and Brauneis, Christian and Pfurtscheller, Gert},
  journal={Journal of neural engineering},
  volume={2},
  number={4},
  pages={123--130},
  year={2005},
  abstract = {Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5\% and 94.4\%. }
}


@article{friman2007multiple,
  title={Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces},
  author={Friman, Ola and Volosyak, Ivan and Gr{\"a}ser, Axel},
  journal={Biomedical Engineering, IEEE Transactions on},
  volume={54},
  number={4},
  pages={742--750},
  year={2007},
  publisher={IEEE},
  abstract = {In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84\%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed}
}


@article{vialatte2010steady,
  title={Steady-state visually evoked potentials: focus on essential paradigms and future perspectives},
  author={Vialatte, Fran{\c{c}}ois-Beno{\^\i}t and Maurice, Monique and Dauwels, Justin and Cichocki, Andrzej},
  journal={Progress in neurobiology},
  volume={90},
  number={4},
  pages={418--438},
  year={2010},
  publisher={Elsevier},
  abstract = {After 40 years of investigation, steady-state visually evoked potentials (SSVEPs) have been shown to be useful for many paradigms in cognitive (visual attention, binocular rivalry, working memory, and brain rhythms) and clinical neuroscience (aging, neurodegenerative disorders, schizophrenia, ophthalmic pathologies, migraine, autism, depression, anxiety, stress, and epilepsy). Recently, in engineering, SSVEPs found a novel application for SSVEP-driven brain–computer interface (BCI) systems. Although some SSVEP properties are well documented, many questions are still hotly debated. We provide an overview of recent SSVEP studies in neuroscience (using implanted and scalp EEG, fMRI, or PET), with the perspective of modern theories about the visual pathway. We investigate the steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation. Next, we describe the SSVEP-BCI paradigm and review recently developed SSVEP-based BCI systems. Lastly, we outline future research directions related to basic and applied aspects of SSVEPs.}
}

@incollection{zander2010enhancing,
  title={Enhancing human-computer interaction with input from active and passive brain-computer interfaces},
  author={Zander, Thorsten O and Kothe, Christian and Jatzev, Sabine and Gaertner, Matti},
  booktitle={Brain-computer interfaces},
  pages={181--199},
  year={2010},
  publisher={Springer}
}