- Open Access
Decoding hand movement velocity from electroencephalogram signals during a drawing task
© Lv et al; licensee BioMed Central Ltd. 2010
Received: 24 March 2010
Accepted: 28 October 2010
Published: 28 October 2010
Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.
Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.
The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.
These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.
Brain-computer interface (BCI) is a system that translates brain signals reflecting user intentions into commands and drives external devices [1, 2]. In the past decades, various BCI systems have been developed for the purpose of rehabilitation and medical care for the disabled patients [3–7]. Among them, researchers have particular interest in neuromotor prosthesis that moves an artificial limb by the brain signals which control the equivalent movement of a corresponding body part such as an arm or a hand . To date, most progresses of these BCI systems have been based on invasive approaches using neuronal firing patterns [4, 8, 9], local field potentials (LFPs) [10, 11] or electrocorticogram (ECoG) [12–14]. These signals inside head possess the advantages of little noise, high topographical resolution and broad bandwidth.
However, for applications on human being, invasive ways are seriously limited by questions about the safety and durability of implanted channels . Some recent studies have demonstrated that brain signals recorded by non-invasive approaches also carry significant information of detailed limb movements. For instance, from magnetoencephalogram (MEG) signals, hand movement directions have been decoded in the discrete center-out reaching task ; hand positions have been decoded during the continuous joystick movements ; and hand velocities have been decoded during the discrete center-out drawing task , the target-to-target joystick movements  and the continuous trackball movements . It has been reported that low frequency band (≤3 Hz or 2-5 Hz) MEG on motor-related areas is critically involved in representing limb movement direction and speed [16, 20]. Moreover, long-distance coupling between primary motor cortex and multiple brain areas in the low frequency band has been found during a continuous visuomotor task . And the neural mechanisms of speed and tau in pointing hand movement from MEG have been revealed (tau is defined as the ratio of the current distance-to-goal gap over the current instantaneous speed towards the goal) .
Compared with MEG, electroencephalogram (EEG) has lower signal-to-noise ratio and spatial resolution. It was generally thought that EEG could not extract sufficient information to reconstruct limb movements. However, EEG is easily available and more suitable for ambulatory prosthetic system [17, 21]. Therefore, a few ambitious researchers have extended the exploration to EEG signals. For example, hand directions have been inferred from EEG recorded in a center-out joystick operation . The subjects were constrained to small finger and wrist movements. Another study has been presented about the prediction of reaching target from EEG recorded in multi-joint center-out movements . Later, a movement delay paradigm was designed to investigate brain activities in the human posterior parietal cortex (PPC) during the planning of intended movements . Newly, the positions, the velocities and the accelerations of hand movement were modestly decoded during a 3-D center-out reaching task [24, 25]. As far as we know, most of these EEG studies employed a center-out movement task which contained pre-specified point-to-point movements. Specifically, the starting and end points were fixed, and the length of each movement was well constrained.
Subjects and Recording System
Five right-handed healthy male subjects participated voluntarily in this study. Among them, subject 1 had been well trained in the BCI experiments of hand motor imageries, while the other subjects had less or never participated in any kind of BCI experiment before. These five subjects were instructed to move a pen (using their right wrist only and relaxing left hand on the lap) on the touch screen of a laptop in front of them. Meanwhile, the pen tracks denoting the trajectories of hand movements were recorded with a sampling rate of 64 Hz by the laptop. At the same time, a 40-channel EEG cap LT37 from Compumetics was used to collect EEG signals from the subjects. And a portable amplifier (NeuroScan NuAmps) amplified the analog EEG signals, digitalized them with a sampling rate of 250 Hz. The laptop received the EEG data from the amplifier through a USB port and sent synchronous stimulus code through parallel port to the amplifier.
The detailed parameters of the drawing task
3.7 ± 2.1
11.3 ± 7.5
7.2 ± 3.2
5.9 ± 3.8
4.6 ± 2.2
10.9 ± 2.9
8.0 ± 1.9
9.4 ± 2.7
9.1 ± 3.1
9.4 ± 2.8
12.0 ± 2.4
8.6 ± 2.5
10.3 ± 2.5
12.0 ± 4.1
11.4 ± 3.6
11.9 ± 2.7
8.2 ± 1.8
8.2 ± 2.4
9.9 ± 3.6
10.2 ± 3.0
11.6 ± 2.8
8.7 ± 1.7
12.8 ± 2.3
11.4 ± 3.1
10.4 ± 3.3
8.2 ± 4.2
2.2 ± 1.5
4.6 ± 2.9
4.2 ± 3.7
7.3 ± 3.4
31.6 ± 15.8
24.5 ± 17.6
33.3 ± 20.2
24.8 ± 21.0
33.8 ± 15.7
EOG and EMG removal
During our drawing tasks, the recorded EEG signals were contaminated with various artifacts such as EOG and EMG . These artifacts may confound the EEG decoding of hand movements . To show an example, we collected the EOG of Subject 3 and provided an off-line analysis in Appendix A1. The off-line analysis of EOG and the decoding of hand velocity of Subject 3 were based on the same dataset. To remove EOG and EMG, we employed ICA. It is a process that detects and isolates independent components (ICs) of signals consisting of mixed sources. For each subject, 30 ICs were decomposed from EEG signals by using the EEGLAB software , and about 12 ICs regarded as EOG/EMG were removed by the following heuristics: (i) Eye movements should project mainly to frontal sites with a low-pass time course; (ii) Eye blinks should project to frontal sites and have large punctate activations; (iii) Temporal muscle activities should project to temporal sites with a spectral peak in the band above 20 Hz . An example of EOG and EMG removal is also given in Appendix A1.
Since the direction was approximately fixed (up, down, left or right) in each point-to-point movement in our study, the values of hand velocities have close relationship with the directions. For example, when a subject performed a movement to the right, the absolute value of hand velocity in y-dimension is small and the hand velocity in x-dimension is large. It may suggest that the brain components discriminative for different directional movements were helpful for reconstructing the profiles of hand velocities. Therefore, supervised spatial filtering methods CSP and DSP were employed here to extract the discriminative brain components. Specifically, after EOG and EMG were removed, a filter bank was applied to filter the retained ICs into multiple bands (0.1-4 Hz, 4-8 Hz, 8-12 Hz, ..., 36-40 Hz). Then DSP was used to extract the amplitude features of slow potentials within 0.1-4 Hz band of the ICs. And CSP was applied to extract the power features of oscillatory rhythms from the other bands of the ICs. The details of DSP and CSP methods can be found in Appendix A2.
In DSP and CSP training procedure, we cut hand movement trajectories into segments with a sliding window (1s wide and 0.5s overlap) to obtain the directions in the drawing task. It was expected that the trajectory in each segment only exhibits one movement direction. However, in practice, the trajectories of some segments may not be straight lines or not extend enough in a direction. The ICs of these segments were not used into DSP or CSP training. Note that DSP and CSP were originally proposed to deal with binary classification problems. As far as our 4-direction hand movements are concerned, DSP and CSP need to be extended to multiclass paradigms. In this study, they were computed between each pair of directions , and the number of the pairs was .
where X i ∈ R C×T is the recorded EEG signal in the i th frequency band, i = 1,2,...,10, C is the number of channels, T is the number of sample points covering the entire time period of an experiment, U ∈ R m×C is the 'unmixing' matrix of ICA, m is the number of retained ICs, is the filtering matrix of DSP or CSP in the i th frequency band, l i is the number of the selected filters (l 1 = 12, l 2 = l 3 =...= l 10 = 24), is the filtered data.
At last, we extracted the features from the filtered data ξ i every 200 ms without overlap, i.e., ξ i = [ψ i 1, ψ i 2,..., ψ iN ], where N is the number of 200 ms bins. Within each 200 ms bin, the average amplitudes of 0.1-4 Hz signals were calculated as , where is the q th row of ψ 1,j , j = 1,2,..., N, q = 1,2,...,12. The variances of the other frequency band signals were computed, normalized and log-transformed as , where is the p th row of ψ 1,j , i = 2,3,...,10, p = 1,2,...,24. Before decoding, these features were normalized to zero mean and unit variance. They were denoted as z j = [z 1,j , z 2,j , ..., z 10,j ], z ∈ R D×N , where D = 228 is the dimension of features. Moreover, in this paper, x-velocity and y-velocity of the hand movement were measured as the displacements of pen track on horizontal dimension and vertical dimension within each 200 ms bin, respectively.
The decoding algorithm presented in this paper consists of a standard Kalman filter and a smoother. The Kalman filter is a real-time processing algorithm in which the state estimate is updated immediately after a new observation is available. On the other hand, the smoother optimally combines the Kalman filter with a reverse-time information filter. The result is a minimum variance estimate based on past, present and future information .
(1) Kalman filter
where and are the predicted mean and covariance of the state before seeing z j ; S j is the prediction covariance of the observation; and P j are the estimated mean and covariance of the state after seeing z j , K j is the filter gain.
where C j is the smoother gain; and P j are the filter estimates for the state mean and state covariance; and are the smoother estimates for the state mean and state covariance. The recursions start from the last time step.
To study the fidelity of the drawing movement decoding and the characteristics of the associated EEG signals, we will show the accuracy of the hand velocity decoding, demonstrate the scalp areas most involved for the decoding and present the frequency bands that carried information of hand velocity. 5-fold cross-validation was employed in the evaluation, i.e., each subject's data were divided into 5 parts, among them 4 parts were used for training, and the retained part was adopted for test. This procedure was repeated 5 times. In each time, a different part was used as the test set. The results of these evaluations are described below.
Decoding accuracy of drawing movement
Decoding performance of hand velocity using ICA-cleaned EEG
0.62 ± 0.05
0.29 ± 0.03
0.50 ± 0.03
0.29 ± 0.03
0.16 ± 0.01
0.37 ± 0.08
0.04 ± 0.02
0.17 ± 0.02
0.39 ± 0.03
0.28 ± 0.03
0.30 ± 0.02
0.24 ± 0.06
1.84 × 10-9
1.17 × 10-7
2.14 ± 0.41
0.30 ± 0.12
1.19 ± 0.13
0.35 ± 0.08
0.09 ± 0.02
0.81 ± 0.38
-0.06 ± 0.03
0.05 ± 0.08
0.66 ± 0.09
0.34 ± 0.06
0.36 ± 0.04
0.27 ± 0.13
From Table 2, we can find that, except the result of Subject 1 in y-dimension, the small p-values indicate that the CCs are significant. On average, the modest CCs and SNRs demonstrate that it is possible to infer information about hand velocities in drawing task by EEG. For most subjects, the hand velocities in horizontal dimension, x, were better decoded than those in vertical dimension, y. Similar disparity in the MEG decoding between dimensions of hand movement has been discussed in . Because the subjects were asked to draw on the vertical touch screen, gravitational force may impact the drawing action of subjects and degrade the decoding in y-dimension . Although we only presented the results for one parameter setting (1s segment length for CSP/DSP filter training and 200 ms step size for Kalman smoother decoding), it was also found that these parameters could be chosen in a wide range. For instance, we also tried other parameter settings (segment length for CSP/DSP filter training: 0.5s and 2s; decoding step size: 100 ms and 300 ms), and obtained comparable results. These results are not included in this paper due to limited page space.
Scalp areas most involved for hand velocity decoding
Figure 3(A) presents the average scalp topographies across the 5 subjects. Generally, the contralateral and ipsilateral channels in motor, posterior parietal and occipital areas have greater weights, and the contralateral dominance is demonstrated. Specifically, for amplitude features in low frequency band (0.1-4 Hz), the channels over premotor, posterior parietal and occipital areas get greater weights; for power features in 4-40 Hz, the channels over posterior parietal and occipital areas get greater weights. These findings suggest the widespread involvement of brain areas with hand kinematics during the drawing task. The results are approximately in accordance with the following studies: Wang et al. demonstrated that intended movement directions can be predicted by recording EEG from posterior parietal areas ; Bradberry et al. showed that the sensorimotor area is important for hand velocity decoding ; And Vaillancourt DE et al. presented that the parietal and premotor cortex are associated with visuomotor processes .
Figure 3(B) displays the scalp topographies separately for each subject. On the whole, the channels on motor, posterior parietal and occipital areas get greater weights both in 0.1-4 Hz band and in 4-40 Hz band for all the subjects, although the weights of these areas are subject-dependent. As an exception, for Subject 4, the channels on prefrontal area also get greater weights. It may have been caused by some artifacts.
Decoding performance of different frequency bands
Comparison on decoding performance with ICA-cleaned data and non-ICA-cleaned data
Decoding performance of hand velocity using non-ICA-cleaned EEG
0.62 ± 0.05
0.35 ± 0.02
0.51 ± 0.03
0.49 ± 0.02
0.30 ± 0.03
0.46 ± 0.06
0.07 ± 0.03
0.22 ± 0.03
0.46 ± 0.03
0.38 ± 0.02
0.35 ± 0.05
0.30 ± 0.07
Comparison on decoding performance of linear filter, Kalman filter and Kalman smoother
Comparison on decoding performance of Kalman smoother and the other methods
Lag = 0 ms
Lag = 200 ms
Lag = 400 ms
Lag = 600 ms
X-D: p = 0.0163
X-D: p = 0.0163
X-D: p = 0.0209
X-D: p = 0.0163
vs. Kalman filter
Y-D: p = 0.0257
Y-D: p = 0.0257
Y-D: p = 0.0120
Y-D: p = 0.0314
X-D: p = 0.0061
X-D: p = 0.0037
X-D: p = 0.0024
X-D: p = 0.0027
vs. Linear filter
Y-D: p = 0.0122
Y-D: p = 0.0074
Y-D: p = 0.0098
Y-D: p = 0.0163
X-D: p = 0.0107
X-D: p = 0.0096
X-D: p = 0.0133
X-D: p = 0.0258
vs. Kalman filter
Y-D: p = 0.0542
Y-D: p = 0.0544
Y-D: p = 0.0791
Y-D: p = 0.1230
X-D: p = 0.0022
X-D: p = 0.0018
X-D: p = 0.0012
X-D: p = 0.0007
vs. Linear filter
Y-D: p = 0.0034
Y-D: p = 0.0017
Y-D: p = 0.0013
Y-D: p = 0.0011
Comparison with other related studies
In , the center-out task is a 3D reaching movement, in which the subject moved his hand from a fixed starting point (center) to one of the 8 stationary targets, and then moved his hand back to the center. In this paper, the task is a 2D self-routed drawing movement, in which the subject was required to move a pen at his own pace along a zigzag route in each trial. This task can be regarded as sequential point-to-point movements. At each point the subject selected one of the four directions. Moreover, the numbers and positions of these points, and the distance between two sequential points were up to the subject. Therefore, compared to , the starting point, the end point and the length of each point-to-point movement in our experiments were less constrained. The subjects can perform the movements with higher variability. It has been reported in  that the variabilities of movement time and movement length are negatively correlated with the accuracy of hand velocity decoding. From this viewpoint, the hand velocity of our drawing movement could be harder to decode than that of the center-out movement task.
In , subjects were asked to perform multi-joint movements of the upper limb. In our work, the subjects were instructed to make movements only with their hands and wrists, while keeping their shoulders and arms at rest. We studied hand movements not only because of the interesting work on hand movement direction decoding , but also because hand is relatively far from the EEG cap, therefore reduces EMG contamination to the EEG signals. Since our drawing task needs the coordination of eye and hand, EOG and EMG may confound the EEG decoding. Thus we employed ICA to remove EOG and EMG artifacts.
Decoding hand kinematics in different frequency bands
Which frequency band of neural signal carries most information about limb kinematics is an important issue discussed in the existing studies. For example, Ball et al. summarized the decoding accuracies of arm movement direction with different band ECoG, and indicated that highest decoding accuracy can be obtained from slow movement-related potentials (MRPs) (<2 Hz) . Jerbi et al. reported the notable phase locking between 2-5 Hz MEG oscillatory activity in the contralateral primary motor cortex and time-varying hand speed . Regarding EEG recording, Waldert et al. discovered that low frequency band (≤3 Hz) EEG of the sensors located in the motor-related area have close relationship with movement directions . In addition, it is well known that the planning and execution of movement leads to significant power modulation in 8-30 Hz EEG, i.e., event-related synchronization/desynchroniza- tion (ERS/ERD) [39, 40]. Such characteristic changes in EEG rhythms have been used to classify brain states related to the planning/imagery of different types of limb movement . Newly, Han et al. reported that EEG activities in the alpha (8-12 Hz) and beta (18-28 Hz) frequency bands were correlated with the speed of imagery clenching . In our study, we have shown that displacement velocity can be represented by the MRP in 0.1-4 Hz band and the ERD/ERS in 24-28 Hz band. Further more, we analyzed the relevance of decoding results from different frequency bands (see Appendix A3), and found that the decoding results of MRPs from low frequency band (0.1-4 Hz) are little correlated with those of oscillation rhythms from higher frequency bands (4-40 Hz). It indicates that the potential shifts in the low frequency band and the power modulations in the higher frequency bands reflect different aspects of brain activities related to hand movement velocity. Furthermore, from the scalp map in Figure 3 (A), we find that in the low frequency band, the channels in the motor, posterior parietal and occipital areas get greater weights. This demonstrates that the features in the low frequency bands capture the neural signature. The finding is in accordance with the ECoG study of Schalk et al. which also focused on decoding kinematic parameters of hand movement .
Decoding limb kinematics from brain signals in non-invasive ways may realize safe and convenient control of motor prosthesis. In this paper, we demonstrated that EEG signals can be used to decode hand velocity during a sequential drawing task. The scalp areas over motor cortex, posterior parietal cortex and occipital areas were most involved for the decoding. Furthermore, we show that not only slow potentials in 0.1-4 Hz band, but also oscillatory rhythms in 24-28 Hz band may carry information about hand velocity.
A1. EOG and EMG removal based on ICA
Correlation between EOG activity and hand velocity
Horizontal hand velocity
CC = 0.04 (p = 0.16)
CC = 0.14 (p = 1.12 × 10-6)
Vertical hand velocity
CC = 0.01 (p = 0.73)
CC = 0.03 (p = 0.30)
A2. Details of DSP and CSP algorithms
Both DSP and CSP are linear projection methods [27, 28]. They have the same data model as Y = WT X , where Y ∈ R C×T denotes the source component, W ∈ R C×C is the projection matrix and X ∈ R C×T represents the EEG segment, with C denoting the number of channels, and T denoting the number of samples in the time interval of interest.
where q = 1,2,..., C, γ q is an eigenvalue and w q is the corresponding eigenvector. Assuming these eigenvalues are sorted in a descending order, only a few eigenvectors W* = [w 1,...,w d ] associated with the largest eigenvalues are chosen as the most discriminative spatial filters, where d << C. Then each EEG segment is projected as Y* = W*T X, Y* ∈ R d×T . To obtain the amplitude features of slow potential shifts, we calculate the mean of Y* as , where r = 1, ..., d, is the r th row of Y*. In our work, d = 2.
where q = 1,2,..., C, β q is an eigenvalue and w q is the corresponding eigenvector. Suppose these eigenvalues are sorted in a descending order, the eigenvectors associated with the largest and smallest m eigenvalues are chosen as the most discriminative spatial filters, i.e., W* = [w 1,...,w m , w C-m+1,...,w C ], where m << C. Then each EEG segment is projected as Y* = W*T X, Y* ∈ R2m×T . To extract the power features, we calculated the logarithm transformation, normalized the variance of Y* by rows . In this paper, m = 2. The logarithm transformation is performed to normalize the distribution of the elements in .
A3. Relevance of decoding results from different frequency bands
This work was supported by National Natural Science Foundation of China under Grants 60825306, Guangdong Natural Science Foundation under Grants 9251064101000012 and Fundamental Research Funds for the Central Universities, SCUT under Grants 2009ZZ0055 and 2009ZZ0059.
- Dornhege G, Millan J, Hinterberger T, McFarland DJ, Müller KR: Toward brain-computer interfacing. Cambridge MA, MIT Press; 2007.Google Scholar
- Waldert S, Pistohl T, Braun C, Ball T, Aertsen A, Mehring C: A review on directional information in neural signals for brain-machine interfaces. J Physiol (Paris) 2009, 103: 244–254. 10.1016/j.jphysparis.2009.08.007View ArticleGoogle Scholar
- Wolpaw JR, McFarland DJ: Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA 2004, 101: 17849–17854. 10.1073/pnas.0403504101View ArticleGoogle Scholar
- Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006, 442: 164–171. 10.1038/nature04970View ArticleGoogle Scholar
- Mason SG, Bashashati A, Fatourechi M, Navarro KF, Birch GE: A comprehensive survey of brain interface technology designs. Ann Biomed Eng 2007, 35: 137–169. 10.1007/s10439-006-9170-0View ArticleGoogle Scholar
- Zhang H, Guan C, Wang C: Asynchronous P300-based brain-computer interfaces: a computational approach with statistical models. IEEE Trans Biomed Eng 2008, 55: 1754–1763. 10.1109/TBME.2008.919128View ArticleGoogle Scholar
- Blakely T, Miller KJ, Zanos SP, Rao RP, Ojemann JG: Robust, long-term control of an electrocorticographic brain-computer interface with fixed parameters. Neurosur Focus 2009, 27: E13. 10.3171/2009.4.FOCUS0977View ArticleGoogle Scholar
- Taylor DM, Tillery SI, Schwartz AB: Direct cortical control of 3D neuro- prosthetic devices. Science 2002, 296: 1829–1832. 10.1126/science.1070291View ArticleGoogle Scholar
- Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB: Cortical control of a prosthetic arm for self-feeding. Nature 2008, 453: 1098–1101. 10.1038/nature06996View ArticleGoogle Scholar
- Mehring C, Rickert J, Vaadia E, Cardosa DO, Aertsen A, Rotter S: Inference of hand movements from local field potentials in monkey motor cortex. Nat Neurosci 2003, 6: 1253–1254. 10.1038/nn1158View ArticleGoogle Scholar
- Rickert J, Oliveira SC, Vaadia E, Aertsen A, Rotter S, Mehring C: Encoding of movement direction in different frequency ranges of motor cortical local field potentials. J Neurosci 2005, 25: 8815–8824. 10.1523/JNEUROSCI.0816-05.2005View ArticleGoogle Scholar
- Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, Moran DW: A brain computer interface using electrocorticographic signals in humans. J Neural Eng 2004, 1: 63–71. 10.1088/1741-2560/1/2/001View ArticleGoogle Scholar
- Pistohl T, Ball T, Schulze-Bonhage A, Aertsen A, Mehring C: Prediction of arm movement trajectories from ECoG-recordings in humans. J Neurosci Methods 2008, 167: 105–115. 10.1016/j.jneumeth.2007.10.001View ArticleGoogle Scholar
- Schalk G, Kubánek J, Miller KJ, Anderson NR, Leuthardt EC, Ojemann JG, Limbrick D, Moran DW, Gerhardt LA, Wolpaw JR: Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J Neural Eng 2007, 4: 264–275. 10.1088/1741-2560/4/3/012View ArticleGoogle Scholar
- Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM: Brain- computer interfaces for communication and control. Clin Neurophysiol 2002, 113: 767–791. 10.1016/S1388-2457(02)00057-3View ArticleGoogle Scholar
- Waldert S, Preissl H, Demandt E, Braun C, Birbaumer N, Aertsen A, Mehring C: Hand movement direction decoded from MEG and EEG. J Neurosci 2008, 28: 1000–1008. 10.1523/JNEUROSCI.5171-07.2008View ArticleGoogle Scholar
- Georgopoulos AP, Langheim FJ, Leuthold AC, Merkle AN: Magnetoencephalo-graphic signals predict movement trajectory in space. Exp Brain Res 2005, 167: 132–135. 10.1007/s00221-005-0028-8View ArticleGoogle Scholar
- Bradberry TJ, Rong F, Contreras-Vidal JL: Decoding center-out hand velocity from MEG signals during visuomotor adaptation. NeuroImage 2009, 47: 1691–1700. 10.1016/j.neuroimage.2009.06.023View ArticleGoogle Scholar
- Tan HR, Leuthold AC, Lee DN, Lynch JK, Georopoulos AP: Neural mechanisms of movement speed and tau as revealed by magnetoencephalo-graphy. Exp Brain Res 2009, 195: 541–552. 10.1007/s00221-009-1822-5View ArticleGoogle Scholar
- Jerbi K, Lachaux JP, N'Diaye K, Pantazis D, Leahy RM, Garnero L, Baillet S: Coherent neural representation of hand speed in humans revealed by MEG imaging. Proc Natl Acad Sci 2007, 104: 7676–7681. 10.1073/pnas.0609632104View ArticleGoogle Scholar
- Stefan R, Hermann S: On the opposition of EEG and MEG. Clin Neurophysiol 2007, 118: 1658–1659. 10.1016/j.clinph.2007.04.021View ArticleGoogle Scholar
- Hammon PS, Makeig S, Poizner H, Todorov E, de Sa VR: Predicting reaching targets from human EEG. IEEE Signal Process Mag 2008, 25: 69–77. 10.1109/MSP.2008.4408443View ArticleGoogle Scholar
- Wang Y, Makeig S: Predicting intended movement direction using EEG from human posterior parietal cortex. Conf Proc HCI (16) 2009, 437–446.Google Scholar
- Bradberry TJ, Gentili RJ, Contreras-Vidal JL: Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. J Neurosci 2010, 30: 3432–3437. 10.1523/JNEUROSCI.6107-09.2010View ArticleGoogle Scholar
- Bradberry TJ, Gentili RJ, Contreras-Vidal JL: Decoding Three-Dimensional Hand Kinematics from Electroencephalographic signals. Conf Proc IEEE EMBS 2009, 5010–5013.Google Scholar
- Kachenoura A, Albera L, Senhadji L, Comon P: ICA: a potential tool for BCI systems. IEEE Signal Process Mag 2008, 25: 57–68. 10.1109/MSP.2008.4408442View ArticleGoogle Scholar
- Liao X, Yao DZ, Wu D, Li CY: Combining spatial filters for the classification of single-trial EEG in a finger movement task. IEEE Trans Biomed Eng 2007, 54: 821–831. 10.1109/TBME.2006.889206View ArticleGoogle Scholar
- Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 2008, 25: 41–56. 10.1109/MSP.2008.4408441View ArticleGoogle Scholar
- Bar-Shalom Y, Li XR, Kirubarajan T: Estimation with applications to tracking and navigation: Theory, Algorithms and Software. New York: Wiley Press; 2001. full_textView ArticleGoogle Scholar
- Fatourechi M, Bashashati A, Ward RK, Birch GE: EMG and EOG artefacts in brain computer interface systems: a survey. Clin Neurophysiol 2006, 118: 480–494. 10.1016/j.clinph.2006.10.019View ArticleGoogle Scholar
- Delorme A, Makeig S: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. J Neurosci Methods 2004, 134: 9–21. 10.1016/j.jneumeth.2003.10.009View ArticleGoogle Scholar
- Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, Sejnowski TJ: Removing Electroencephalographic Artifacts by Blind Source Separation. Psychophysiol 2000, 37: 163–78. 10.1017/S0048577200980259View ArticleGoogle Scholar
- Sadeghian EB, Moradi MH: Continuous detection of motor imagery in a four-class asynchronous BCI. Conf Proc IEEE Eng Med Biol Soc 2007, 3241–3244.Google Scholar
- Tarvainen MP, Georgiadis SD, Ranta-Aho PO, Karjalainen PA: Time-varying analysis of heart rate variability signals with a kalman smoother algorithm. Physiol Meas 2006, 27: 225–239. 10.1088/0967-3334/27/3/002View ArticleGoogle Scholar
- Bradberry TJ, Contreras-Vidal JL, Rong F: Decoding hand and cursor kinematics from magnetoencephalographic signals during tool use. Conf Proc IEEE Eng Med Biol Soc 2008, 5306–5309.Google Scholar
- Vaillancourt DE, Mayka MA, Corcos : Intermittent visuomotor processing in the human cerebellum, parietal cortex and premotor cortex. J Neurophysiol 2006, 95: 922–931. 10.1152/jn.00718.2005View ArticleGoogle Scholar
- Wu W, Black MJ, Gao Y, Bienenstock E, Serruya M, Shaikhouni A, Donoghue JP: Neural decoding of cursor motion using a Kalman filter. In Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press; 2003:133–140.Google Scholar
- Ball T, Schulze-Bonhage A, Aertsen A, Mehring C: Differential representation of arm movement direction in relation to cortical anatomy and function. J Neural Eng 2009, 6: 016006. 10.1088/1741-2560/6/1/016006View ArticleGoogle Scholar
- Pfurtscheller G, Lopes da Silva FH: Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clin Neurophysiol 1999, 110: 1842–1857. 10.1016/S1388-2457(99)00141-8View ArticleGoogle Scholar
- Pineda JA, Allison BZ, Vankov A: The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP's): Toward a brain-computer interface (BCI). IEEE Trans Rehabil Eng 2000, 8: 219–222. 10.1109/86.847822View ArticleGoogle Scholar
- Townsend G, Graimann B, Pfurtscheller G: Continuous EEG classification during motor imagery--simulation of an asynchronous BCI. IEEE Trans Neur Syst and Rehabil Eng 2004, 12: 258–265. 10.1109/TNSRE.2004.827220View ArticleGoogle Scholar
- Han Y, Christopher P, Bin H: Relationship between speed and EEG activity during imagined and executed hand movements. J Neural Eng 2010, 7: 026001. 10.1088/1741-2560/7/2/026001View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.