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Table of Contents
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Introduction
Neurological phenomena are specific features of the brain activity that appear in the brain signals and can be used to control a BCI system. They are time-locked to a physical stimulus or to the cognitive responses of the brain. Neurological phenomena are characterized by their voltage amplitude, their latency which is related to the internal or external stimuli and their spatiotemporal distribution. Their amplitude is usually much smaller than the background EEG signal.
Neurological phenomena can be categorized in two groups based on the origin of the phenomenon in the brain. Those neurological phenomena generated as the result of cognitive responses of the brain are called endogenous. The ones evoked by an external stimulus are called exogenous.
BCI systems that use exogenous neurological phenomena, usually do not need any user training [ALL03]. The downside of using these systems is that they require a constant commitment of one of sensory pathways to an external stimulus [VAU96]. Furthermore, not all users may tolerate repetitive sensory stimulation. On the other hand, endogenous-based BCI systems rely on the generation of a phenomenon that is more natural and is thus expected to cause the users less fatigue. This may be the reason why more than 80% of BCI studies use endogenous neurological phenomena to control BCI systems [MAS07].
To generate a suitable neurological phenomenon, endogenous-based BCI systems usually need user training. This training may take a long time, sometimes even up to few months. The use of complex signal processing schemes for detecting weak neurological phenomena can greatly reduce or even eliminate the training process[DOR04]. Another advantage of employing endogenous neurological phenomena is that it is possible to select and use a combination of some of them to improve the performance of the system.
Current endogenous and exogenous neurological phenomena in BCI systems include:
Endogenous Neurological Phenomena
1)Changes in the power of brain rhythms
1.1)Mu and Beta Rhythms
The Mu rhythms is an 8-12 Hz EEG rhythm seen in awake people when they are not engaged in processing sensorimotor input or producing motor output. The main location that the Mu rhythms can be observed is the sensorimotor cortical areas. Mu rhythms are usually associated with 18-26 Hz Beta rhythms (see references in [VAU98] for more information) . Beta rhythms are considered to be related to the Mu rhythms, but the exact relation still remains unknown. [KRU07] has argued that Mu and Beta rhythms are consistently phase-coupled and combining Mu and Beta bands as independent control features is sub-optimal.
1.2) Event-related Desynchronization (ERD)
A voluntary movement results in a circumscribed desynchronization in the upper Mu and lower Beta bands which starts about two seconds prior to movement. This phenomenon has been thus named as event-related desynchronization (ERD) [PFU99]. ERD can be interpreted as an electro-physiological correlate of activated cortical areas involved in processing of sensory or cognitive information or production of motor behavior [PFU99].
It is shown that the time course of the ERD of the is almost identical with brisk and slow movements [PFU99]. The ERD is also shown to be similar for index, thumb and hand movements [PFU99]. Thus, the ERD of the Mu rhythm reflects a relatively unspecific pre-activation, pre-setting of neurons in motor areas.
Finger movement of the dominant hand results in a pronounced ERD in the contralateral hemisphere. But the ERD in the ipsilateral hemisphere is low. Finger movement of the non-dominant hand results in a less laterized ERD, though.
1.3) Event-related Synchronization (ERS)
Event-related synchronization (ERS) of the Beta rhythms is a relatively robust phenomena. It can be found in nearly every individual after they have performed a finger or a foot movement. ERS of the Beta rhythm is dominant over the contralateral sensorimotor area and has a maximum that occurs about 600 ms after the movement offset. Each subject has his/her own specific reactive frequency component. For finger movement, the largest ERS of the Beta rhythms was found in the 16-21 Hz frequency band [PFU05]. The Beta ERS is also found to be significantly larger with hand movements compared to finger movements.
1.4) Mathematical Definition
The ERD and ERS are defined as the proportional power decrease (ERD) or power increase (ERS) in the relation the baseline activity, determined from a specific reference interval. The baseline activity is usually placed several seconds before the triggering onset [PFU03]. The process of the calculation of ERD is as follows:
1. bandpass- filtering
2. squaring of the samples.
3. Averaging over N trials.
4. Calculation of the relative power.
1.5) Analysis
Mu rhythms are usually identified by spectral methods such as autoregressive (AR) filtering, and Fourier transform. In [KRU07], it is argued that the spectral-based approaches may become problematic, as they cannot differentiate between the visual alpha-rhythm and the Mu-rhythm (they lie in the same frequency band). The effect of visual alpha rhythm can be decreased by spatial filtering. They are also incapable of modelling the Mu rhythm effectively [KRU07]. [KRU07] thus has proposed and showed the effectiveness of defining a matched filter to overcome these limitations. According to [KRU07], since the matched filter theoretically provides the optimal SNR, it is not prone to the extraneous signal contamination that could affect other methods.
1.6) Research Groups
Many research groups have developed BCI systems using the features extracted from the Mu and Beta rhythms. However, the works of two research groups are more prominent. Wolpaw and McFarland and their associates in Wadsworth Center have focused on developing such a CBR-based BCI system. Their proposed BCI system allows users to control the amplitude of Mu and Beta rhythms. This amplitude is then used to move a cursor on the computer screen [MCF98, MCF05, WOL94, WOL04]. Users of this system usually need training that may take up to a few weeks, but eventually they can achieve high accuracies (e.g., above 90%) [MIN98]. The other research group, the Graz BCI, uses the ERD and the ERS of the Mu/Beta rhythms [MUL05,PFU05b, PFU00b]. Similar to the first group, after a few sessions of training, the users of the Graz BCI can also achieve high accuracies.
1.7) Brain rhythms of Healthy vs. Disabled Individuals
In [KAU06], it is shown that the desynchronization in tetraplegics subjects is not typically followed by a typical contralateral rebound as seen in the data of health subjects. For this reason, the trials were not classified according to the Mu/Beta frequency bands. However, in [PFU00], it is shown tat a tetraplegic patient learnt to increase the amplitude of Mu rhythm over a 5-month period.
1.8) frequency of Mu and Beta rhythm
In various BCI papers, different frequency bands have been attributed to the Mu and Beta rhythms. Examples include:
[ABD06] : Mu frequency band:8-13 Hz and Beta frequency band: 14-34 Hz.
[INC06] : Mu frequency band: 7-13 Hz and Beta frequency band: 14-32 Hz.
[TAK06] : Mu frequency band: 8-13 Hz and Beta frequency band: 13-30 Hz.
2) Movement-related potentials
Averaging the EEG data with respect to movement onset results in the generation of slow potentials called “movement-related potentials” (MRPs) [DEE76]. MRPs start about 1.5–1 seconds before the onset of a particular movement and have bilateral distribution) [DEE76, SHI80,TAR90, HAL94, BAB99]. High-resolution EEG studies have modeled the main sources of MRPs arising in the supplementary motor area and the primary sensorimotor cortex [URB96, URB98]. MRPs have been used for the neurological phenomenon in several BCI studies. These studies include the work that has been carried out by Mason and Birch’s research group [BOR04, BIR02, MAS00], Muller and Blankertz et. Al [BLA03, DOR03] as well as Yom-tov and Inbar [YOM01, YOM02,YOM03] .
2.1) Readiness Potential (RP)
Movement-related potentials (MRPs) consist of different components that have been studied in various clinical and engineering applications, including readiness potentials (RP), etc..
One of the important components of MRPs is Bereitschaftspotential (BP) or readiness potential (RP). It is a transient postsynaptic response of main pyramidal peri-central neurons. In performing hand movements, it is focused contralateral to the performing hand (see [BLA06b] for more information). RP is less pronounced in attempted movements compared to executed movements probably due to the fact that the actual movement does not happen [BLA06b].
3) Other movement related activities (OMRAs)
The movement-related activities that do not belong to any of the preceding categories can be categorized as other movement-related activities (OMRA). OMRAs are usually not restricted to a particular frequency band or scalp location and usually cover different frequency ranges. They may be a combination of specific and non-specific neurological phenomena. Levine and Huggins’ research group are amongst the prominent research groups that have used OMRAs related to different movements to design their ECoG-based BCI system. They recorded ECoG activity from patients with 16-126 subdural electrodes prior to an epilepsy surgery. They have used topographically focused potentials associated with different movements to develop various 2-state self-paced BCI designs [GRA04, LEV99, HUG95].
4) Slow cortical potentials (SCPs)
SCPs are slow usually non-movement potential changes generated by the user. They reflect changes in the cortical polarization of the EEG, lasting from 300 ms up to several seconds [WOL02, NEU03]. Birbaumer and his colleagues have developed a BCI system called “Thought Translation Device (TTD)” that uses an SCP as the source of control [HIN04, HIN05, BIR00, BIR04]. They have shown that patients with sever motor disabilities such as late-stage ALS can learn to control their SCPs and thus use TTD to communicate with the outside world.
5) Cognitive tasks (CTs)
Changes in the brain signals as a result of non-movement mental tasks (e.g., mental counting, solving a multiplication problem) are usually categorized as CTs [KUB01]. The works of Milan et.al and Anderson et. al [AND95] are amongst the prominent BCI research carried out using cognitive tasks. Millan et.al’s work involves using the mental tasks to control a mobile robot, while Anderson et.al have focused on the design of a multi-class BCI system that detects cognitive tasks associated with different tasks such as 3D object recognition, mental counting, etc [GAR03, AND98].
6) P300
When infrequent or particularly significant auditory, visual or somatosensory stimuli are interspersed with frequent or routine stimuli, they evoke a positive peak at about 300 ms after the stimulus is received. This peak is called P300 [ALL03]. Using this so-called “oddball” response, Donchin and his colleagues have used P300 to build a successful BCI system [FAR88, DON00]. More recently, a number of studies have shown that P300-based control can be used as an alternative communication channel for people with spinal cord injury and ALS [SEL06, PIC06]. Also, for individuals with visual impairments, solutions based on auditory or tactile stimuli have been proposed [GLO86, ROD96].
Exogenous Neurological Phenomena
1) Visual Evoked Potentials
VEPs are brain responses to visual stimulation and they reflect the visual information processing in the brain [WAN06].Many BCI systems use VEPs to control the BCI system including the works of Sutter [SUT99] and Middendorf [MID00].
1.1) Disadvantage
One downside of using VEPs in Brain-Computer interface system is that subjects using VEPs need voluntary gaze control. However, patients in the late stage of ALS or other locked-in conditions lose their eye controls [MUL06].
2) Steady State Visual Evoked Potentials
SSVEP is a VEP modulated at frequencies above six Hz. The maximum amplitude of SSVEP occurs at the occipital region [WAN06]. The frequency range associated with SSVEP usually comprise the fundamental frequency of the visual stimulus as well as its harmonics [LIN06].
2.1) How SSVEP Works
An SSVEP-based BCI system has an LED panel. LEDs located on this panel flicker at different frequencies. Each frequency is associated with a specific command. When a user looks at a specific LED, an SSVEP with a fundamental frequency similar to that LED is induced, which is detected by the BCI system [LIN06].
2.2) Disadvantages
One downside of using SSVEPs in Brain-Computer interface system is that subjects using SSVEPs need voluntary gaze control. However, patients in the late stage of ALS or other locked-in conditions lose their eye controls [MUL06].
Other Neurological Phenomena
This section, describes other neurological phenomena that do not specifically belong to any of the "endogenous" and "exogenous" categories. These phenomena are as follows:
1) Multiple neurological phenomena (MNs)
BCI systems based on multiple neurological phenomena use a combination of two or more of the above neurological phenomena for the purpose of control.
2) Activity of neural cells (ANC)
Some BCI research groups have used microelectrode arrays to record the activity of single neurons in the motor cortex for the purpose of BCI control [CHA99, NIC02, FRA04,DAR03]. These BCI systems are usually based on reconstructing a movement from recorded spike trains. Experiments with monkeys have shown a relatively good ability of control in multiple directions in these systems [SER02]. Recently, there have been reports of a patient learning to use his neuronal activity to move a computer cursor to several directions using the ANCs [HOC06]. These encouraging results provide hope for BCI control with multiple options and high accuracy. The downside is the invasive nature of the microelectrode implants, which may result in infection and side effects in the brain.
References
[ABD06] Abdollahi, F., and Motie-Nasrabadi, A., “Combination of frequency bands in EEG for feature reduction in mental task classification” , in the Proc. 28th IEEE EMBS Int. Conf., New York, USA, Aug 30-Sept 3, 2006, pp.1146-1149.
[ALL03] B. Z. Allison and J. A. Pineda, "ERPs evoked by different matrix sizes: Implications for a brain computer interface (BCI) system", IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, no.2, pp. 110-113, Jun.2003.
[AND95] C. W. Anderson, S. V. Devulapalli and E. A. Stolz, "Signal Classification with Different Signal Representations", Neural Networks for Signal Processing, pp. 475-483, 1995.
[AND98] C. W. Anderson, E. A. Stolz and S. Shamsunder, "Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks", IEEE Trans. Biomed. Eng., vol. 45, no.3, pp. 277-286, Mar.1998.
[BAB99] C. Babiloni, F. Carducci, F. Cincotti, P. M. Rossini, C. Neuper, G. Pfurtscheller and F. Babiloni, "Human movement-related potentials vs desynchronization of EEG alpha rhythm: a high-resolution EEG study", Neuroimage, vol. 10, no.6, pp. 658-665, Dec.1999.
[BIR93] G. E. Birch, P. D. Lawrence and R. D. Hare, "Single Trial Processing of Event Related Potentials Using Outlier Information", IEEE Trans. Biomed. Eng., vol. 40, no.1, pp. 59-73, 1993.
[BIR00] N. Birbaumer, A. Kubler, N. Ghanayim, T. Hinterberger, J. Perelmouter, J. Kaiser, I. Iversen, B. Kotchoubey, N. Neumann and H. Flor, "The thought translation device (TTD) for completely paralyzed patients", IEEE Trans. Rehabil. Eng., vol. 8, no.2, pp. 190-193, Jun.2000.
[BIR02] G. E. Birch, Z. Bozorgzadeh and S. G. Mason, "Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 10, no.4, pp. 219-224, Dec.2002.
[BIR04] N. Birbaumer, "The thought-translation-device (TTD): Taming cognition for action", Brain Cogn., vol. 54, no.2, pp. 130-130, Mar.2004.
[BLA03] B. Blankertz, G. Dornhege, C. Schäfer, R. Krepki, J. Kolmorgen, K. R. Müller, V. Kunzmann, F. Losch and G. Curio, "Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis," in IEEE Trans. Neural Sys. Rehab. Eng, vol.11, no.2, 2003.
[BLA06b] Blankertz, B., et. al, “The Berlin Brain-Computer interface: EEG-based communication without subject training”, IEEE Trans. Neural System and Rehab. Eng., Vol. 14, No.2, June 2006, pp. 147-152.
[BOR04] J. F. Borisoff, S. G. Mason, A. Bashashati and G. E. Birch, "Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch", IEEE Trans. Biomed. Eng., vol. 51, no.6, pp. 985-992, Jun.2004.
[CHA99] J. K. Chapin, K. A. Moxon, R. S. Markowitz and M. A. Nicolelis, "Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex", Nat. Neurosci., vol. 2, no.7, pp. 664-670, Jul.1999.
[DAR03] S. Darmanjian, Sung Phil Kim, M. C. Nechyba, S. Morrison, J. Principe, J. Wessberg and M. A. L. Nicolelis, "Bimodal brain-machine interface for motor control of robotic prosthetic," in the Proc. IEEE Int. Conf. Intelligent Robots and Systems, vol.4,pp. 3612-3617 , 2003.
[DEE76] L. Deecke, B. Grozinger and H. H. Kornhuber, "Voluntary finger movement in man: cerebral potentials and theory", Biol. Cybern., vol. 23, no.2, pp. 99-119, Jul 14.1976.
[DON00] E. Donchin, K. M. Spencer and R. Wijesinghe, "The mental prosthesis: assessing the speed of a P300-based brain-computer interface", IEEE Trans. Rehabil. Eng., vol. 8, no.2, pp. 174-179, Jun.2000.
[DOR03] G. Dornhege, B. Blankertz and G. Curio, "Speeding up classification of multi-channel brain-computer interfaces: Common spatial patterns for slow cortical potentials," in Proc. 1st IEEE EMBS Int. Conf. on Neural Engineering,pp. 595-598. 2003.
[DOR04] G. Dornhege, B. Blankertz, G. Curio and K. R. Muller, "Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms", IEEE Trans. Biomed. Eng., vol. 51, no.6, pp. 993-1002, Jun.2004.
[FAR88] L. A. Farwell and E. Donchin, "Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials", Electroencephalogr. Clin. Neurophysiol., vol. 70, no.6, pp. 510-523, Dec.1988.
[FRA04] J. T. Francis and J. K. Chapin, "Force field apparatus for investigating movement control in small animals", IEEE Trans. Biomed. Eng., vol. 51, no.6, pp. 963-965, Jun.2004.
[GAR03] D. Garrett, D. A. Peterson, C. W. Anderson and M. H. Thaut, "Comparison of linear, nonlinear, and feature selection methods for EEG signal classification", IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, no.2, pp. 141-144, Jun. 2003.
[GLO86] A. A. Glover, M. C. Onofrj, M. F. Ghilardi and I. Bodis-Wollner, "P300-like potentials in the normal monkey using classical conditioning and an auditory 'oddball' paradigm", Electroencephalogr. Clin. Neurophysiol., vol. 65, no.3, pp. 231-235, May.1986.
[GRA04] B. Graimann, J. E. Huggins, S. P. Levine and G. Pfurtscheller, "Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis", IEEE Trans. Biomed. Eng., vol. 51, no.6, pp. 954-962, Jun.2004.
[HAL94] M. Hallett, "Movement-related cortical potentials", Electromyogr. Clin. Neurophysiol., vol. 34, no.1, pp. 5-13, Jan-Feb.1994.
[HIN04] T. Hinterberger, S. Schmidt, N. Neumann, J. Mellinger, B. Blankertz, G. Curio and N. Birbaumer, "Brain-computer communication and slow cortical potentials", IEEE Trans. Biomed. Eng., vol. 51, no.6, pp. 1011-1018, Jun.2004.
[HIN05] T. Hinterberger, B. Wilhelm, J. Mellinger, B. Kotchoubey and N. Birbaumer, "A device for the detection of cognitive brain functions in completely paralyzed or unresponsive patients", IEEE Trans. Biomed. Eng., vol. 52, no.2, pp. 211-220, Feb.2005.
[HOC06] L. R. Hochberg, M. D. Serruya, G. M. Friehs, J. A. Mukand, M. Saleh, A. H. Caplan, A. Branner, D. Chen, R. D. Penn and J. P. Donoghue, "Neuronal ensemble control of prosthetic devices by a human with tetraplegia", Nature, vol. 442, no.7099, pp. 164-171, Jul 13.2006.
[HUG95] J. E. Huggins, S. P. Levine, R. Kushwaha, S. L. Bement, L. A. Schuh and D. A. Ross, "Identification of cortical signal patterns related to human tongue protrusion," in pp. 670-672. 1995.
[INC06] Ince, N. F., et. al, “Using multiresolution space-time-frequency features for the classification motor imagery EEG recordings in a brain computer interface task”, in the Proc. 14th IEEE Conf. on Signal Processing and Communications Applications, 17-19 April 2006, pp.1-4
[KAU06] Kauhanen, et. al, “EEG and MEG Brain-computer interface for tetraplegic patients” , IEEE Trans. Neural System and Rehab. Eng., Vol. 14, No.2, June 2006, pp. 190-193.
[KRU07] Krusienski, D. J., et. al, “A Mu-rhythm matched filter for continuous control of a brain-computer interface”, IEEE Trans. Biomed. Eng., Vol. 54, No.2, Feb. 2007.
[KUB01] A. Kubler, B. Kotchoubey, J. Kaiser, J. R. Wolpaw and N. Birbaumer, "Brain-Computer Communication: Unlocking the Locked In", Psych Bulletin, vol. 127, no.3, pp. 358-375, May.2001.
[LEV99] S. P. Levine, J. E. Huggins, S. L. Bement, R. K. Kushwaha, L. A. Schuh, E. A. Passaro, M. M. Rohde and D. A. Ross, "Identification of Electrocorticogram Patterns as the Basis for a Direct Brain Interface", J Clinical Neurophysiol, vol. 16, no.5, pp. 439-447, Sep.1999.
[LIN06] Lin, Z., et. al, “Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs”, IEEE Trans. Biomed. Eng., Vol. 53, No.12, Dec. 2006, pp. 2610-2614.
[MAS00] S. G. Mason and G. E. Birch, "A brain-controlled switch for asynchronous control applications", IEEE Trans. Biomed. Eng, vol. 47, no.10, pp. 1297-1307, Oct.2000.
[MAS07] S. G. Mason, A. Bashashati, M. Fatourechi, K. F. Navarro and G. E. Birch, "A comprehensive survey of brain interface technology designs", Ann. Biomed. Eng., vol. 35, no.2, pp. 137-169, Feb.2007.
[MCF98] D. J. McFarland, L. M. McCane and J. R. Wolpaw, "EEG-based communication and control: short-term role of feedback", IEEE Trans. Rehabil. Eng., vol. 6, no.1, pp. 7-11, Mar.1998.
[MCF05] D. J. McFarland, W. A. Sarnacki, T. M. Vaughan and J. R. Wolpaw, "Brain-computer interface (BCI) operation: signal and noise during early training sessions", Clin. Neurophysiol., vol. 116, no.1, pp. 56-62, Jan.2005.
[MID00] M. Middendorf, G. McMillan, G. Calhoun and K. S. Jones, "Brain-Computer Interfaces Based on the Steady-State Visual-Evoked Response", IEEE Trans. Rehab. Eng., vol. 8, no.2, pp. 211-214, Jun. 2000.
[MIN98] L. A. Miner, D. McFarland and J. R. Wolpaw, "Answering Questions with an Electroencephalogram-Based Brain Computer Interface", Arch. Phys. Med. Rehabil., vol. 79, pp. 1029-1033, 1998.
[MUL05] G. R. Muller-Putz, R. Scherer, G. Pfurtscheller and R. Rupp, "EEG-based neuroprosthesis control: a step towards clinical practice", Neurosci. Lett., vol. 382, no.1-2, pp. 169-174, Jul 2005.
[MUL06] Muller-Putz, G. R., et. al, “Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces?”, IEEE Trans. Neural syst. and Rehab. Eng., Vol. 14, No.2, March 2006, pp.30-37.
[NEU03] N. Neumann, A. Kubler, J. Kaiser, T. Hinterberger and N. Birbaumer, "Conscious perception of brain states: mental strategies for brain-computer communication", Neuropsychologia, vol. 41, no.8, pp. 1028-1036, 2003.
[NIC02] M. A. L. Nicolelis and J. K. Chapin, "Controlling robots with the mind", Sci. Am., vol. 287, no.4, pp. 46-53, Oct.2002.
[PFU99] G. Pfurtscheller and F. H. Lopes da Silva, “Event-related EEG/MEG synchronization and desynchronization: Basic principles” , Clin. Neurophysiol., Vol.110, pp. 1842-1857, 1999.
[PFU00] Pfurtscheller, G., et. al, “”brain oscillations control hand orthosis in a tetraplegic”, Neuroscience letters, Vol.292, pp.211-214, 2000.
[PFU00b] G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A. Schlogl, B. Obermaier and M. Pregenzer, "Current trends in Graz Brain-Computer Interface (BCI) Research", IEEE Trans. Rehab. Eng., vol. 8, no.2, pp. 216-219, Jun. 2000.
[PFU03] Pfurtscheller G, Graimann B, Huggins J E , Levine S P, Schuh L A. “Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement“, Clinical Neurophysiology, Vol. 114, No.7,pp.1226-1236, 2003.
[PFU05] Pfurtscheller G, and da Silva, F. L., “EEG Event-related desynchronization (ERD) and Event-related synchronization (ERS)”, Electroencephalography: Basic Principles , Clinical Applications, and Related Fields Niedermeyer, E., and da Silva, F. L. (Eds.), Lippincott Williams & Wilkins, 5th edition, 2005.
[PFU05b] G. Pfurtscheller, G. R. Müller-Putz, J. Pfurtscheller and R. Rupp, "EEG-Based Asynchronous BCI Controls Functional Electrical Stimulation in a Tetraplegic Patient", EURASIP Journal on Applied Signal Processing, vol. 2005, no.19, pp. 3152-3155, 2005.
[PIC06] F. Piccione, F. Giorgi, P. Tonin, K. Priftis, S. Giove, S. Silvoni, G. Palmas and F. Beverina, "P300-based brain computer interface: reliability and performance in healthy and paralysed participants", Clin. Neurophysiol., vol. 117, no.3, pp. 531-537, Mar.2006.
[ROD96] B. Roder, F. Rosler, E. Hennighausen and F. Nacker, "Event-related potentials during auditory and somatosensory discrimination in sighted and blind human subjects", Brain Res. Cogn. Brain Res., vol. 4, no.2, pp. 77-93, Sep.1996.
[SEL06] E. W. Sellers, A. Kubler and E. Donchin, "Brain-computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: the P300 Speller", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, no.2, pp. 221-224, Jun.2006.
[SER02] M. D. Serruya, N. G. Hatsopoulos, L. Paninski, M. R. Fellows and J. P. Donoghue, "Instant neural control of a movement signal", Nature, vol. 416, no.6877, pp. 141-142, Mar.2002.
[SHI80] H. Shibasaki, G. Barrett, E. Halliday and A. M. Halliday, "Components of the movement-related cortical potential and their scalp topography", Electroencephalogr. Clin. Neurophysiol., vol. 49, no.3-4, pp. 213-226, Aug.1980.
[SUT92] E. E. Sutter, "The brain response interface: communication through visually-induced electrical brain responses", J Micro Comp App, vol. 15, pp. 31-45, 1992.
[TAK06] Takahashi, K., et. al, “Remarks on hands-free manipulation system using simple brain-computer interface”, in the Proc. IEEE Int. Conf. Control Applications, Munich, Germany, October 4-6 2006, pp.295-300.
[TAR90] I. M. Tarkka and M. Hallett, "Cortical topography of premotor and motor potentials preceding self-paced, voluntary movement of dominant and non-dominant hands", Electroencephalogr. Clin. Neurophysiol., vol. 75, no.2, pp. 36-43, Feb.1990.
[URB96] A. Urbano, C. Babiloni, P. Onorati and F. Babiloni, "Human cortical activity related to unilateral movements. A high resolution EEG study", Neuroreport, vol. 8, no.1, pp. 203-206, Dec 20.1996.
[URB98] A. Urbano, C. Babiloni, P. Onorati, F. Carducci, A. Ambrosini, L. Fattorini and F. Babiloni, "Responses of human primary sensorimotor and supplementary motor areas to internally triggered unilateral and simultaneous bilateral one-digit movements. A high-resolution EEG study", Eur. J. Neurosci., vol. 10, no.2, pp. 765-770, Feb.1998.
[VAU96] T. M. Vaughan, J. R. Wolpaw and E. Donchin, "EEG-Based Communication: Prospects and Problems", IEEE Trans. Rehab. Eng., vol. 4, no.4, pp. 425-430, 1996.
[VAU98] Vaughan, T. M., et. al, “EEG-based communication: analysys of concurrent EMG activity”, Electroencephalography and clinical neurophysiology, Vol.107, 1998, pp.428-433.
[WAN06] Wang, Y., et. al, “A practical VEP-based brain-computer interface, IEEE Trans. Neural syst. and Rehab. Eng., Vol. 14, No.2, March 2006, pp.234-239.
[WOL94] J. R. Wolpaw and D. J. McFarland, "Multichannel EEG-based brain-computer communication", Electroencephalogr. Clin. Neurophysiol., vol. 90, no.6, pp. 444-449, Jun.1994.
[WOL02] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller and T. M. Vaughan, "Brain-computer interfaces for communication and control", Clin. Neurophysiol., vol. 113, no.6, pp. 767-791, Jun.2002.
[WOL04] J. R. Wolpaw and D. J. McFarland, "Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans", in Proc. Natl. Acad. Sci. U. S. A., vol. 101, no.51, pp. 17849-17854, Dec 21.2004.
[YOM01] E. Yom-Tov and G. F. Inbar, "Selection of relevant features for classification of movements from single movement-related potentials using a genetic algorithm," in the Proc. 23rd IEEE/EMBS Int. Conf.,vol.2,pp. 1364-1366 , 2001.
[YOM02] E. Yom-Tov and G. F. Inbar, "Feature Selection for the Classification of Movements From Single Movement-Related Potentials", IEEE Trans. Neural Syst. Rehab. Eng., vol. 10, no.3, pp. 170-177, Sep.2002.
[YOM03] E. Yom-Tov and G. F. Inbar, "Detection of Movement-Related Potentials from the Electro-Encephalogram for possible use in a Brain-Computer Interface", Medical and Biological Engineering and Computing, vol. 41, no.1, pp. 85-93, Jan.2003.
