A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Methods <s> Abstract An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments. <s> BIB001 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Methods <s> Abstract A brain–computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) is presented which allows human subjects to observe and control changes of their own blood oxygen level-dependent (BOLD) response. This BCI performs data preprocessing (including linear trend removal, 3D motion correction) and statistical analysis on-line. Local BOLD signals are continuously fed back to the subject in the magnetic resonance scanner with a delay of less than 2 s from image acquisition. The mean signal of a region of interest is plotted as a time-series superimposed on color-coded stripes which indicate the task, i.e., to increase or decrease the BOLD signal. We exemplify the presented BCI with one volunteer intending to control the signal of the rostral–ventral and dorsal part of the anterior cingulate cortex (ACC). The subject achieved significant changes of local BOLD responses as revealed by region of interest analysis and statistical parametric maps. The percent signal change increased across fMRI-feedback sessions suggesting a learning effect with training. This methodology of fMRI-feedback can assess voluntary control of circumscribed brain areas. As a further extension, behavioral effects of local self-regulation become accessible as a new field of research. <s> BIB002 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Methods <s> Single-trial classification of EEG signals has received increasing attention in both basic research and for the development of EEG based brain computer interfaces (BCI). Typically, such classification has been performed using signals from a set of selected EEG sensors. Since EEG sensor signals are mixtures of signals from multiple intra- and extra-cranial sources, single-trial sensory and motor evoked potentials can be difficult to detect and classify. In this paper, second-order blind identification (SOBI) was used to preprocess EEG data and extract activity from the left and right primary somatosensory (SI) cortices. Subsequently, classification of event-related potentials (ERPs) evoked by a sequence of randomly mixed left, right, and bilateral median nerve stimulations was performed by back-propagation neural networks, using as inputs the two SOBI-recovered SI components or the two "best sensors". Results from four subjects showed that classification accuracy was significantly higher when SOBI-recovered left and right SI components were used for classification than when the EEG sensor signals were used directly. <s> BIB003
The BCI designs selected for this review include every journal and conference paper that met the following criteria: (1) One or more of the keywords BCI, BMI, DBI appeared in its title, abstract or keyword list. (2) The work described one or more BCI designs (the minimum design content that met the criteria was a BCI transducer as described in section 2). There were a few papers that only reported pre-processing techniques specifically designed for brain-computer interfaces that use neural cortical recordings. These papers were reported in the pre-processing techniques. Papers that presented tutorials, descriptions of electrode technology, neuroanatomy, and neurophysiology discussions that might serve as the basis for a BCI were not included. (3) Only papers published in English and in refereed international journals and conference proceedings were included. (4) Designs that use functional magnetic resonance imaging (fMRI) BIB002  , Yoo et al 2004 , magneto-encephalography (MEG) signals (Georgopoulos et al 2005) , near-infra-red spectrum (NIRS), auditory evoked potentials (Hill et al 2004  and somatosensory evoked potentials BIB003 were not included in this paper. (5) Papers were published prior to January 2006. Although no paper meeting the five criteria explained above was omitted from the analysis, some papers may have been missed unintentionally. The current work should thus be regarded as an initial step to build a public database that can be updated and evolved with time. In tables 3-8, we categorized the papers according to the signal processing methods used. For each of the design blocks of a BCI system shown in figure 1 (signal enhancement, feature extraction, feature selection/dimensionality reduction, feature classification, and post-processing), we created a table that reports the signal processing techniques used in that block. Since most of the BCI designs that use neural cortical recordings do not contain a feature extraction component, we generated a separate table (table 7) to report the translation schemes used in these designs. Feature extraction methods used in BCI systems are closely related to the specific neuromechanism(s) used by a BCI. For example, the feature extraction algorithms employed in VEP-based BCIs are used to detect the visual evoked potentials in the ongoing EEG. In BCI systems that operate on slow cortical potentials (SCP), the extracted features are mostly used for the purpose of identifying this specific Mu rhythms in the range of 8-12 Hz and beta rhythms in the range of 13-30 Hz both originate in the sensorimotor cortex and are displayed when a person is not engaged in processing sensorimotor inputs or in producing motor outputs  . They are mostly prominent in frontal and parietal locations (Kozelka and Pedley 1990 , Kubler et al 2001a  . A voluntary movement results in a circumscribed desynchronization in the mu and lower beta bands  . This desynchronization is called event-related desynchronization (ERD) and begins in the contralateral rolandic region about 2 s prior to the onset of a movement and becomes bilaterally symmetrical immediately before execution of movement BIB001 . After a voluntary movement, the power in the brain rhythms increases. This phenomenon, called event-related synchronization (ERS), is dominant over the contralateral sensorimotor area and reaches a maximum around 600 ms after movement offset BIB001 . Gamma rhythm is a high-frequency rhythm in the EEG. Upon the occurrence of a movement, the amplitude of gamma rhythm increases. Gamma rhythms are usually more prominent in the primary sensory area. Movement-related potentials (MRPs) MRPs are low-frequency potentials that start about 1-1.5 s before a movement. They have bilateral distribution and present maximum amplitude at the vertex. Close to the movement, they become contralaterally preponderant (Babiloni et al 2004 , Deecke and Kornhuber 1976 , Hallett 1994 .