Linear classification of low-resolution EEG patterns produced by imagined hand movements

Babiloni, Fabio; Cincotti, Febo; Lazzarini, L.; Millán, José del R.; Mouriño, J.; Varsta, Markus; Heikkonen, Jukka; Bianchi, Luigi; Marciani, M. G.

doi:10.1109/86.847810

Cited by 140 publications

(80 citation statements)

References 6 publications

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“…If some subjects have different topography of ERD, this rule may not be so effective. A better classifier with some training procedure could be introduced to further improve the approach (Babiloni et al 2000(Babiloni et al ,2001Pfurtscheller 1997;.…”

Section: Discussionmentioning

confidence: 99%

“…Present-day EEG-based BCIs use different signals to encode the subjects' intent, such as slow cortical potentials, P300 potentials, and mu or beta rhythms (Wolpaw et al 1991(Wolpaw et al , 2002Pfurtscheller et al 1994Pfurtscheller et al ,1997Pfurtscheller et al ,1999McFarland et al 1997;Birbaumer 1999;Babiloni et al 2000Babiloni et al ,2001. In the present study, we focus on mu rhythms caused by motor imagination, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The noninvasive methods include the use of electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and optical imaging. Among them, because EEG has relatively short time constants, which can function in most environments, and EEG acquisition systems are relatively simple and inexpensive, EEG based BCI systems are currently widely used (Wolpaw et al 1991(Wolpaw et al ,2002Pfurtscheller et al 1994Pfurtscheller et al ,1997Birbaumer 1999;Babiloni et al 2000Babiloni et al ,2001. On the other hand, intracranial recording methods, such as electrocorticograms and single-neuron recordings, are invasive (Donoghue 2002;Nicolelis 2001Nicolelis ,2003Rohde et al 2002) although these methods take advantage of better signal quality.…”

Section: Introductionmentioning

confidence: 99%

“…During unilateral hand movement imagery, an event-related desynchronization (ERD) appears on the contralateral hemisphere. So the difference in the scalp EEG with the different hand imagination can be used to generate different control signals for BCI systems (Pfurtscheller et al 1997;Babiloni et al 2000Babiloni et al ,2001.…”

Section: Introductionmentioning

confidence: 99%

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A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications

Qin

2005

J. Neural Eng.

129

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We report a pilot study of performing classification of motor imagery for Brain Computer Interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications

Qin

2005

J. Neural Eng.

129

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“…for instance, self modulation by imagination of movements can result in changes in EEG rhythm in central region of the scalp overlying the sensorimotor cortex [3] [8] [33] [45]. These rhythms are the basis of several BCI systems [3] [8] in which imagination of hand movement gives rise to an amplitude suppression in the α-band (8-12 Hz) and β-band (13-28 Hz) 4 rhythms over the contralateral primary hand motor cortical area [33]. Wolpaw and co-workers [43] [45] used continuous changes in the amplitudes of these rhythms to move a cursor in a computer screen.…”

Section: Bci Research and Ibci Systemmentioning

confidence: 99%

Constructing Ambient Intelligence

Ferscha¹,

Aitenbichler²,

Mühlhäuser³

2008

Communications in Computer and Information Science

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Abstract. This paper is aimed to introduce IDIAP Brain Computer Interface (IBCI) research that successfully applied Ambience Intelligence (AmI) principles in designing intelligent brain-machine interactions. We proceed through IBCI applications describing how machines can decode and react to the human mental commands, cognitive and emotive states. We show how effective human-machine interaction for brain computer interfacing (BCI) can be achieved through, 1) asynchronous and spontaneous BCI, 2) shared control between the human and machine, 3) online learning and 4) the use of cognitive state recognition. Identifying common principles in BCI research and ambiance intelligence (AmI) research, we discuss IBCI applications. With the current studies on recognition of human cognitive states, we argue for the possibility of designing empathic environments or devices that have a better human like understanding directly from brain signals. MotivationBrain Computer Interfacing (BCI) or Brain Machine Interfacing (BMI) refers to interaction with devices, where user's intentions represented as several brain states are deciphered and translated into actions without requiring any physical action [44] [25] [21]. There is a growing interest in the use of brain signals for communicating and operating devices, which is facilitated by the advances in the the measurement technologies in the past decades. As BCI bypasses the classical neuromuscular communication channels, this technology is intended to use for rehabilitation of tetraplegic or paraplegic patients to improve their communication, mobility and independence. The BCI research also opens up new possibilities in natural interaction for able-bodied people (e.g., for space applications, where environment is inherently hostile and dangerous for astronauts, who could greatly benefit from direct mental teleoperation of external semi-automatic manipulators [26], and for entertainment applications like multimedia gaming [20]). Typical applications of BCI are communication aids such as spelling devices [5] [31] [25] and mobility aids such as wheelchair [41]. In the current paper, we Gangadhar.Garipelli@idiap.ch

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EEG ‐Based Brain‐Computer Interface System

Pfurtscheller

Gramlich

Neuper

2006

Wiley Encyclopedia of Biomedical Engineering

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A brain computer interface (BCI) transforms electrophysiological signals originating from the human brain into commands that control devices or applications. In this way, a BCI provides a new nonmuscular communication channel, which can be extremely useful for people with severe neuromuscular disorders such as amyotrophic lateral sclerosis and brainstem stroke. The immediate goal of our current research is to provide these users with an opportunity to communicate with their environment. Future applications also include the field of multimedia and virtual reality. Present day BCIs use a variety of electrophysiological signals such as slow cortical potentials, evoked potentials (P300), oscillatory activity recorded from scalp or subdural electrodes, and cortical neuronal activity recorded from implanted electrodes. EEG is by far the most frequently used input source, because it is readily available and noninvasive. The poor signal‐to‐noise ratio of scalp–recorded signals requires the application of advanced signal processing methods. This article outlines and explains the current approaches and methods used in BCI research with emphasis on the signal processing part of the system, consisting of preprocessing, feature extraction, and classification. However, the success of a BCI depends not only on the methodologies applied, but also on the capability of the user to develop and maintain the skill to produce the brain patterns employed by the BCI. Therefore, the interaction between the BCI system and the user, in terms of adaptation and learning, is a challenging aspect of any BCI development and application.

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Linear classification of low-resolution EEG patterns produced by imagined hand movements

Abstract: Abstract-Electroencephalograph

Cited by 140 publications

References 6 publications

A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications

A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications

Constructing Ambient Intelligence

EEG ‐Based Brain‐Computer Interface System

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