Event-related synchronization (ERS) in the alpha band--an electrophysiological correlate of cortical idling: a review

EEG desynchronization is a reliable correlate of excited neural structures of activated cortical areas. EEG synchronization within the alpha band may be an electrophysiological correlate of deactivated cortical areas. Such areas are not processing sensory information or motor output and can be considered to be in an idling state. One example of such an idling cortical area is the enhancement of mu rhythms in the primary hand area during visual processing or during foot movement. In both circumstances, the neurons in the hand area are not needed for visual processing or preparation for foot movement. As a result of this, an enhanced hand area mu rhythm can be observed.

Motor imagery activates primary sensorimotor area in humans

The spatiotemporal patterns of Rolandic mu and beta rhythms were studied during motor imagery with a dense array of EEG electrodes. The subjects were instructed to imagine movements of either the right or the left hand, corresponding to visual stimuli on a computer screen. It was found that unilateral motor imagery results in a short-lasting and localized EEG change over the primary sensorimotor area. The Rolandic rhythms displayed an event-related desynchronization (ERD) only over the contralateral hemisphere. In two of the three investigated subjects, an enhanced Rolandic rhythm was found over the ipsilateral side. The pattern of EEG desynchronization related to imagination of a movement was similar to the pattern during planning of a voluntary movement.

Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates

Oscillations in the alpha and beta band (<35 Hz) display a dynamic behavior and show characteristic spatiotemporal patterns in sensory, motor and cognitive tasks. The event-related desynchronization (ERD) of alpha band and beta rhythms can be seen as a correlate of an activated cortical area with an increased excitability level of neurons. An event-related synchronization (ERS) of frequency components between 10 and 13 Hz may represent a deactivated cortical area or inhibited cortical network, at least under certain circumstances. It is hypothesized, that antagonistic ERD/ERS patterns, called 'focal ERD/surround ERS', may reflect a thalamo-cortical mechanism to enhance focal cortical activation by simultaneous inhibition of other cortical areas. Induced oscillations in the beta band (13-35 Hz, beta ERS) were found in sensorimotor areas after voluntary movement and after somatosensory stimulation. This may be interpreted as a state of 'inhibition' of neural circuitry in the primary motor cortex. Simultaneous activation of the motor cortex by e.g. motor imagery lead to an attenuation of the beta ERS. Moreover, there is evidence that the frequency of the induced beta oscillations represent a 'resonance-like frequency' of underlying cortical networks. However, further research is needed to investigate the functional meaning of bursts of beta oscillations below 35 Hz.

Post-movement beta synchronization. A correlate of an idling motor area?

Post-movement beta (around 20 Hz) synchronization was investigated in 2 experiments with self-paced finger extension and flexion and externally paced wrist movement. The electrodes were fixed over the sensorimotor area in distances of 2.5 cm. It was found that after a brisk finger movement the desynchronized beta rhythm displayed a fast recovery and a short-lasting synchronization within 1 sec. This post-movement beta synchronization was maximal over the contralateral hemisphere and localized slightly more anterior to the maximal desynchronization of the hand area mu rhythm. The post-movement beta synchronization is interpreted as a correlate of "idling" motor cortex neurons.

EEG-based discrimination between imagination of right and left hand movement

Three subjects were asked to imagine either right or left hand movement depending on a visual cue stimulus. The interval between two consecutive imagination tasks was > 10 s. Each subject imagined a total of 160 hand movements in each of 3-4 sessions (training) without feedback and 7-8 sessions with feedback. The EEG was recorded bipolarly from left and right central and parietal regions and was sampled at 128 Hz. In the feedback sessions, the EEG from both central channels was classified on-line with a neural network classifier, and the success of the discrimination between left and right movement imagination was given within 1.5 s by means of a visual feedback. For each subject, different frequency components in the alpha and beta band were found which provided best discrimination between left and right hand movement imagination. These frequency bands varied between 9 and 14 Hz and between 18 and 26 Hz. The accuracy of on-line classification was approximately 80% in all 3 subjects and did not improve with increasing number of sessions. By averaging over all training and over all feedback sessions, the EEG data revealed a significant desynchronisation (ERD) over the contralateral central area and synchronisation (ERS) over the ipsilateral side. The ERD/ERS patterns over all sessions displayed a relatively small intra-subject variability with slight differences between sessions with and without feedback.

Foot and hand area mu rhythms

Spontaneous EEG can display spatio-temporal patterns of desynchronized or synchronized alpha band activity. Event-related desynchronization (ERD) of rhythms within alpha and lower beta bands is characteristic of activated cortical areas ready to process information or to prepare a movement, while event-related synchronization (ERS) in the same frequency bands can be seen as an electrophysiological correlate of resting or idling cortical areas. EEG was investigated over primary sensorimotor and premotor areas during discrete hand and foot movements. ERD was found over the primary hand area during finger movement and over the primary foot area during toe movement. The former was observed in every subject, the latter was more difficult to find. From these results it can be speculated that each primary sensorimotor area has its own intrinsic rhythm, which becomes desynchronized when the corresponding area is activated. ERS, in the form of an enhanced mu rhythm on electrodes overlying the primary hand area, was observed not only during visual processing but also during foot movement. In both cases, the hand area is not needed to perform a task and, therefore, can be considered to be in an idling state. The supplementary motor area (SMA) also plays an important role in preparation and planning of movement. It is demonstrated that this area also displays rhythmic activity within the alpha band, that is both linearly and non-linearly phase coupled to the intrinsic (mu) rhythm of the primary hand area. With planning and preparation of movement, this SMA rhythm is desynchronized and also the degree of coupling between the two areas decreases.

Current trends in Graz Brain-Computer Interface (BCI) research

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been evaluated.

Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man

Spontaneous EEG activity was recorded at 56 electrodes in 3 healthy subjects. All subjects displayed event-related desynchronization (ERD) of mu rhythms over the cortical hand area during discrete finger movement. In contrast to this, foot movement resulted in an enhancement or event-related synchronization (ERS) of mu rhythms over the hand area. This phasic synchronization of mu waves was circumscribed and found at electrodes overlying both cortical hand areas. It is speculated, that this ERS represents a short lasting 'idling state' of hand area neurons when other body parts are moved.

Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.

Human movement-related potentials vs desynchronization of EEG alpha rhythm: a high-resolution EEG study

Movement-related potentials (MRPs) and event-related desynchronization (ERD) of alpha rhythm were investigated with an advanced high-resolution electroencephalographic technology (128 channels, surface Laplacian estimate, realistic head modeling). The working hypothesis was that MRPs and alpha ERD reflect different aspects of sensorimotor cortical processes. Both MRPs and alpha ERD modeled the responses of primary sensorimotor (M1-S1), supplementary motor (SMA), and posterior parietal (PP, area 5) areas during the preparation and execution of unilateral finger movements. Maximum responses were modeled in the contralateral M1-S1 during both preparation and execution of the movement. The SMA and PP responses were modeled mainly from the MRPs and alpha ERD, respectively. The modeled ipsilateral M1-S1 responses were larger and stronger in the alpha ERD than MRPs. These results may suggest that alpha ERD reflects changes in the background oscillatory activity in wide cortical sensorimotor areas, whereas MRPs represent mainly increased, task-specific responses of SMA and contralateral M1-S1.

Brain oscillations control hand orthosis in a tetraplegic

The aim of the present study was to investigate whether self-induced brain potential changes could be useful as control signals for patients with severe motor impairment, i.e. due to high-level spinal cord injury. The pilot project was performed in a tetraplegic patient (T.S.), whose residual muscle activity of the upper limbs is restricted to the left biceps. To restore the hand grasp function, an electrical driven hand orthosis fitting his left hand was developed. The operation of this device is directly based on the bioelectrical signals of the brain. After some months of training, T. S. has learned to operate the hand orthosis by mental imagination of specific motor commands.

Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment

OBJECTIVE

This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain-computer interface (BCI) for verbal communication.

METHODS

EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a 'telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time-frequency maps of event-related (de-)synchronization (ERD/ERS).

RESULTS

The patient learned to 'produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70% (correct responses) for letter selection. 'Copy spelling' was performed with a rate of approximately one letter per min.

CONCLUSIONS

The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. 'Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.

Rapid prototyping of an EEG-based brain-computer interface (BCI)

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

Functional dissociation of lower and upper frequency mu rhythms in relation to voluntary limb movement

OBJECTIVE

The goal of this study is to investigate the reactivity of central rhythms in the alpha band during self-paced voluntary finger and foot movement and to give an answer to the question, whether different types of mu rhythms exist.

METHODS

The effect of self-paced, voluntary finger and foot movement was studied in a group of 12 right-handed healthy volunteers. The EEG was recorded from a grid of 34 electrodes placed over sensorimotor areas with inter-electrode distances of approximately 2.5 cm. The event-related desynchronization (ERD) was quantified in the 8-10 and 10-12 Hz bands.

RESULTS

Both frequency components are blocked prior to and during movement and therefore, they have to be considered as mu rhythms. The lower frequency component results in a widespread movement-type non-specific ERD pattern, whereas the upper frequency component shows a more focused and movement-type specific pattern, clearly different with finger and foot movement.

CONCLUSIONS

The distinct reactivity patterns provide evidence for the existence of two types of mu rhythms, a somatotopically non-specific lower frequency mu rhythm and a somatotopically specific mu rhythm characteristically found in the upper alpha frequency band.

Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters

Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.

Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas

OBJECTIVE

We studied event-related synchronization (ERS) of beta rhythms related to voluntary movement vs. stimulation of upper and lower limbs. The aim of this study was to investigate whether the frequency of the beta response is related to specific regions within the sensorimotor strip.

METHODS

Self-paced movement and electrical stimulation of the dominant hand and foot/leg was investigated in 10 right-handed volunteers. The electroencephalogram was recorded from closely spaced electrodes over central areas and processed time-locked to movement-offset or stimulation. In order to identify the dominant frequency of the induced beta oscillations, time-frequency maps were calculated using the continuous wavelet transformation. For the specific beta frequency bands, the band power time courses were analyzed by quantifying the event-related (de-)synchronization (ERD/ERS).

RESULTS

Both limb movement and somatosensory stimulation induced bursts of beta oscillations appearing within 1 s after movement/stimulation with a clear focus close to the corresponding sensorimotor representation area. The peak frequency was significantly lower over the hand area (below approximately 20 Hz) than at mid-central sites overlying the foot representation area (above approximately 20 Hz). But no difference was found between movement and stimulation of the respective limb.

CONCLUSIONS

Analyzing the frequency of induced beta activity revealed concomitant oscillations at slightly different frequencies over neighboring cortical areas. These oscillations might be indicative for a resonance-like behavior of connected sub-networks in sensorimotor areas.

Simultaneous EEG 10 Hz desynchronization and 40 Hz synchronization during finger movements

Nineteen-channel EEG was recorded with closely spaced electrodes overlaying the left sensorimotor cortex during self-paced, voluntary right finger movements. Three right-handed people served as subjects. The EEG was analysed in the 10 Hz band (10-12 Hz) and in four 40 Hz bands (34-36, 36-38, 38-40, 40-42) by calculation of ERD time courses and ERD maps, whereby a ERD is characterized by a movement-related band power decrease. In all three subjects a close to C3 localized 10 Hz ERD was found, starting about 2 s prior to movement onset and continuing during movement. Along with this 10 Hz ERD a localized and short-lasting (about 0.5 s) burst of 40 Hz oscillations was embedded around movement onset. This can be interpreted as indicating that planning of movement is accompanied by a desynchronization of central mu rhythm and a generation of 40 Hz oscillations.

Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery

EEG feedback studies demonstrate that human subjects can learn to regulate electrocortical activity over the sensorimotor cortex. Such self-induced EEG changes could serve as control signals for a Brain Computer Interface. The experimental task of the current study was to imagine either right-hand or left-hand movement depending on a visual cue stimulus on a computer monitor. The performance of this imagination task was controlled on-line by means of a feedback bar that represented the current EEG pattern. EEG signals recorded from left and right central recording sites were used for on-line classification. For the estimation of EEG parameters, an adaptive autoregressive model was applied, and a linear discriminant classifier was used to discriminate between EEG patterns associated with left and right motor imagery. Four trained subjects reached 85% to 95% classification accuracy in the course of the experimental sessions. To investigate the impact of continuous feedback presentation, time courses of band power changes were computed for subject-specific frequency bands. The EEG data revealed a significant event-related desynchronization over the contralateral central area in all subjects. Two subjects simultaneously displayed synchronization of EEG activity (event-related synchronization) over the ipsilateral side. During feedback presentation the event-related desynchronization/event-related synchronization patterns showed increased hemispheric asymmetry compared to initial control sessions without feedback.

Toward a hybrid brain-computer interface based on imagined movement and visual attention

Brain-computer interface (BCI) systems do not work for all users. This article introduces a novel combination of tasks that could inspire BCI systems that are more accurate than conventional BCIs, especially for users who cannot attain accuracy adequate for effective communication. Subjects performed tasks typically used in two BCI approaches, namely event-related desynchronization (ERD) and steady state visual evoked potential (SSVEP), both individually and in a 'hybrid' condition that combines both tasks. Electroencephalographic (EEG) data were recorded across three conditions. Subjects imagined moving the left or right hand (ERD), focused on one of the two oscillating visual stimuli (SSVEP), and then simultaneously performed both tasks. Accuracy and subjective measures were assessed. Offline analyses suggested that half of the subjects did not produce brain patterns that could be accurately discriminated in response to at least one of the two tasks. If these subjects produced comparable EEG patterns when trying to use a BCI, these subjects would not be able to communicate effectively because the BCI would make too many errors. Results also showed that switching to a different task used in BCIs could improve accuracy in some of these users. Switching to a hybrid approach eliminated this problem completely, and subjects generally did not consider the hybrid condition more difficult. Results validate this hybrid approach and suggest that subjects who cannot use a BCI should consider switching to a different BCI approach, especially a hybrid BCI. Subjects proficient with both approaches might combine them to increase information throughput by improving accuracy, reducing selection time, and/or increasing the number of possible commands.

Event-related beta synchronization after wrist, finger and thumb movement

Pre-movement event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were studied in a group of normal subjects during voluntary thumb, index finger and wrist movement. The band power time courses were computed for the upper alpha band (10-12 Hz) and for two frequency bands in the range of beta (16-20 Hz and 20-24 Hz). While a similar mu ERD was found during motor preparation for the 3 movement tasks, significant differences concerning beta synchronization were observed after movement off set. The contralateral percentage beta increase (ERS) was significantly larger in gross movements of the wrist as compared to index finger and thumb movements, which is discussed under the assumption of a cumulative effect. Summarizing, pre-movement desynchronization seems relatively independent of the forthcoming type of movement, whereas the post-movement beta synchronization might depend on the activated muscle mass.

Information transfer rate in a five-classes brain-computer interface

The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain-computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the electroencephalogram (EEG) patterns is based on band power estimates and hidden Markov models (HMMs). We propose a method that combines the EEG patterns based on separability into subsets of two, three, four, and five mental tasks. The information transfer rates of the BCI systems comprised of these subsets are reported. The achieved information transfer rates vary from 0.42 to 0.81 bits per trial and reveal that the upper limit of different mental tasks for a BCI system is three. In each subject, different combinations of three tasks resulted in the best performance.

Differentiation between finger, toe and tongue movement in man based on 40 Hz EEG

Movements of right and left index fingers, right toe and tongue were studied by EEG measurement in the alpha and gamma (30-40 Hz) bands. The EEG was recorded with a 56-electrode array over pre- and postcentral areas. For each movement the average power decrease, as a measurement of the event-related desynchronization or power increase in narrow frequency bands, was calculated. Single-trial data from 8 electrodes, 3 frequency bands and 4 time points within a 1 sec window were subject to a classification task. It was found that, based on single EEG trials, the data from the 4 movements could be differentiated with an accuracy of 70% when alpha and gamma band activity were used but only with 58% in the case of the alpha band activity alone. This shows that the gamma band activity or 40 Hz EEG is strongly related to planning of a specific movement and therefore, improves the accuracy of classification significantly.

Event-related desynchronization (ERD) during visual processing

Event-related desynchronization (ERD) is the short-lasting attenuation or blocking of rhythms within the alpha (beta) band. ERD is found during but also before visual stimulation. Two different types of ERD can be differentiated: one short-lasting, localized to occipital areas and involving upper alpha components; the other longer lasting, more widespread, most prominent over parietal areas and maximal for lower alpha components. The former most likely reflects primary visual processing and feature extraction, the latter is more related to cognitive processing and mechanisms of attention.

Human cortical 40 Hz rhythm is closely related to EMG rhythmicity

We recorded cortical neuromagnetic rhythms during self-paced index-finger movements from a subject previously reported to show prominent 40 Hz electroencephalographic activity during motor behavior. The 10 and 20 Hz components of the rolandic mu rhythm were bilaterally suppressed, whereas the contralateral 40 Hz (35-41 Hz) activity was slightly enhanced before both fast and slow movements and strongly enhanced during slow movements. The 40 Hz rhythm originated mainly in the hand motor cortex and was clearly correlated with the rhythmicity of the electromyogram from the extensor muscles, with a systematic time lag. In this subject motor preparation, and especially control of finger movements, may thus be associated with enhanced cortical rhythms near 40 Hz. The coherence of these rhythms with muscular firing patterns likely reflects communication between the sensorimotor cortex and the motor units.

Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness

The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.