BCI based on Movement Related Cortical Potentials (MRCPs)

Introduction

Since the discovery of the slow negative electroencephalographic (EEG) activity preceding self-initiated movement by Kornhuber and Deecke, various source localization techniques in subjects have disclosed the generated mechanisms of each identifiable component of movement-related cortical potentials (MRCPs) to some extent. We understand the “self-initiated movement” or “premovement” as the time when no muscle movement is evident or is unrelated if it occurs, but the subject is fully familiar with the action he is going to perform shortly. This is also referred to as planning/preparation of the movements. In this time arrival (i.e., 0.5-2 s before the movement onset), the cortex is adapted for implementation of action [1].

In the past decade, non-invasive BCI systems have been proposed as an assistive technology to decrease the disability by inducing brain plasticity [2]. Brain plasticity is the brain’s ability to change its structure or function following natural changes such as stroke or learning new motor skills or following artificial induction using techniques such as electrical stimulation. This mechanism that underline skill learning and memory processes are based on the Hebbian principle [3] that synapses are toughened and directions are formed among neurons only when there is a correlated way of activation. Conversely, when they are activated in an uncorrelated fashion, synaptic connections are lost.

Uncorrelated activation leads to a loss of synaptic connections. This principle has been used at a basic level in a BCI context. An electrically induced sensory feedback, otherwise called neurofeedback, was a suitable way of inducing plasticity, only, and only if, it was timed to arrive at the motor area cortex during the peak negative phase of the MRCP generated by task imagination [4]. This timing dependence between the voluntary motor command and this neurofeedback has since been confirmed in both self-paced imageries in healthy subjects and in chronic stroke patients [5]. Since this time difference between the detection of a real movement action and the subsequent active response of an external device for artificial sensory inflow must be precise and short, the latency of the detection between intention and action from active potentials in these applications is one of the most challenging techniques. In general, the acceptable motion-intent detection delay is 200 ms [6]. In particular, the detection latency is crucial in inducing Hebbian associative neural plasticity for rehabilitation purposes [3]. The efficiency of plasticity induction is related to the time arrival of the artificially afferent triggered by the brain switch. Thus, it would be extremely slow if the brain switch arrives either too late or too early about motor intention [4].

Fundamentals of MRCPS

The implementation of a motor task in humans measured over the primary motor cortex is preceded by a moderate depression that can be observed in the EEG amplitude up to 2 s before the onset of the imagined or real executed movement. This potential is known as a the Movement-related cortical potential (MRCP). The MRCP can be further divided into the Bereitschaftspotential (BP) [7] and contingent negative variation (CNV) [8]. The CNV is the one generated when the MRCP is produced incorporation with the planning and execution of a cue-based movement while the BP is created in response to self-paced movement. The MRCP is present in real as well as in imaginary volitional movements [7].

MRPs and Its Components

The MRCP comprises three events called readiness potential (RP) or Bereitschaftspotential (BP), the motor potential (MP), and movement-monitoring potential (MMP), which are thought to reflect movement planning/preparation, execution, and control of performance, correspondingly [9,10]. The MRCP has been investigated in normal persons as well as in patients diagnosed with Amyotrophic Lateral Sclerosis, tremor, Parkinson’s disease, and stroke, supporting the execution of their motor tasks [11,12,13].

The Bereitschaftspotential (BP) or readiness potential (RP) consists of a slow decrease in EEG amplitude starting approximately 1500 ms before the onset of the movement and is considered as a cortical representation of motor preparation [14]. It is also believed that BP may reflect an intention to act, which remains unconscious for part of its time course or an index of resource mobilization [15]. Therefore, it has been suggested that the motor areas are active even if the movement is real as well as imagined. Because they are directly related to volitional movement, MRCPs can be used to detect a movement or intention to move [15]. It has two major segments: the “early BP” as the first part, and the “late BP” as the second part. The early BP is a slow-rising negative segment which develops about 1.5 s before the movement onset and is more distinguished in the central-medial scalp. The late BP has a steeper slope and happens around 400-500 ms before the movement onset and has its maximum amplitude over the primary motor cortex [14]. Both BP phases are differentially influenced by different factors, in particular, by the complexity of the movement which enhances the late BP exclusively. The start of BP regarding the movement onset varies considerably among different conditions of movement and subjects [14]. More details can be found in the comprehensive book [7].

mrcp.png
Figure 1.Waveforms and terminology of movement-related cortical potentials (MRCPs) from a single healthy subject. Time 0 is defined as the movement onset, MP is motor potential, and MMP is movement-monitoring potential. BP1 and BP2 are early BP and late BP, respectively. For more information on experiment protocol, see [100].

The contingent negative variation (CNV) is a slow-rising negative wave that starts with a “Warning” stimulus and ends with a “Go” stimulus [16]. It shows expectancy for an imminent signal and preparation for execution of a response. In other words, CNV reveals preparation for signaled movements and is an indicator for anticipation. The earlier part of the CNV is generated in response to a “Warning” cue and has maximum amplitude over the frontal cortex reflecting the phase of the movement. Conversely, the later or terminal CNV, reflecting preparation for a motor response, begins around 1.5 s before the “Go” cue and has maximum amplitude over the motor cortex [7]. The later CNV happens even if the subject response at the time he anticipates the “Go” stimulus [16].

Generator Sources of MRCPs

At the beginning of MRCPs studies, it was believed the BP might be recorded from subcortical structures such as basal ganglia and thalamus [14]. However, afterward, several studies reported that sensorimotor areas were probably generators of MRCP. Furthermore, it is known that CNV is generated in the dorsal premotor cortex (PMd) [17], while both early and late BP are generated in the primary motor cortex, the supplementary motor area (SMA), and primary somatosensory cortex [18,19].

cortex.png
Figure 2.The main functional divisions of the cortex as seen from a lateral view. Note that most of the primary visual cortex is on the medial surface of each cerebral hemisphere, and so only the small lateral portion is visible in this figure. (From Carlson, 1998, p. 69)

It is known to a certain degree that automatic movements such as blinking of eyelids, spontaneous eye movements, swallowing, chewing, and respiration are also controlled by volitional factors. Therefore, BP is recorded when these movements are reiterated at a self-paced rate [14].

For one hand, self-paced finger movements were related to activation of the anterior SMA, but without the need for activating the sensorimotor cortex [20]. There were few distinctions in the areas of activation between externally triggered activations and self-paced activation, such as a premovement potential preceding the stimulus [20]. For a self-paced finger movement, [21] reported SMA activation anteceded that of the motor cortex by 800 ms.

fads
Figure 3. The dorsal premotor cortex (PMd) is located in the primary motor cortex.

On the other hand, the PMd is believed to play a substantial role in cued movement preparation rather than in self-initiated movements [17]. As we said above, the CNV is generated in the PMd, while the late BP is generated in the primary motor cortex, SMA, and primary somatosensory cortex [17]. Researchers discovered the effects of variation of PMd on BP and CNV reflecting self-initiated versus cued movement preparation by increasing and decreasing the excitability of brain using 5 Hz and 1 Hz repetitive transcranial magnetic stimulations (rTMS), respectively. This study found that neuronal activity of the PMd in humans is favorably included in the preparation of externally cued movements as compared to self-initiated movements.

If we compare the MRCP for a foot movement with hand movement, interesting differences can be shown to some movement components [22]. In particular, many differences are visible in the late BP. For the hand movement, the late BP is highest over the contralateral central area (approximately C1 or C2 of the International 10 – 20 System) and for the foot movement, late BP is maximal at the midline (approximately Cz) [22].

Factors Influencing BP

Different factors influence the components of MRCPs (in particular, BP) [14]. Some of them are the level of intention, preparatory state, the frequency of movement repetition, movement selection, speed and precision of movement, perceived effort, the force exerted, discreteness and complexity of movement, learning and skill acquisition, and pathological injuries of various brain structures. Recently, few studies [23,24,25,26] intended to analyze the effect of the kinetics of movement such as force and speed on
MRCPs.

Methods

The main goal of this project was to identify movement-related cortical potentials (MRCPs). The initial negative phase of MRCPs can be extracted from multi-channel scalp EEG in healty subjects by using a self-paced asynchronous BCI paradigm. To determine such MRCP on a subject repeating several specific movements, we used data provided by Imran Khan Niazi (for further information see [27]). The electrodes were placed accurately, according to the 10-20 EEG system, in the motor cortical area, exactly where sensory and motor functions can be reflected in EEG signals.

electrodes2.png
Figure 4. The electrodes were located at FP1, F3, Fz, F4, C3, Cz, C4, P3, Pz and P4 according to the 10-20 system, see Figure 5.2, a reference was placed on the right mastoid bone and the ground electrode was positioned at the nasion. EOG recordings were recorded from the FP1 electrode.

Processing of Data

Pre-Processing

Previously recorded EEG signals were bandpass filtered between 0.05 and 5 Hz with a second order Butterworth filter. Also, a Notch filter was applied at the cutoff frequency of 50 Hz and, thus, filter the continuous EEG data using linear FIR filter and eliminate the powerline noise.

Processing (EEGLAB – MATLAB Toolbox)

We used EEGLAB to extract epochs from -1500ms to 1000ms of each finger-tapping movement onset, and we rejected some of the epochs manually. Furthermore, we run ICA in EEG Data in order to extract the independent components and study the ICA activity. We rejected the noise components in order to increase the signal-noise ratio of the EEG signal.

Component maps

The component maps in 2D plots display the scalp map projection of the selected independent components. Learning to recognize types of independent components may require experience. The main criteria to determine if a component is cognitively related, a muscle artifact, or some other type of artifact are, first, the scalp map (as shown below), next the component time course, next the component activity power spectrum and, finally (given a dataset of event-related data epochs), the ERP image [109].

runica2.png
Figure 5. Independent Components extracted from our EEG data. From EEGLAB Toolbox.

Analysis of EEG Signal

After this preprocessing of EEG data, each epoch file was studied by plotting channel spectra, maps, various Event-Related Potential (ERP) plots like channel ERP with scalp maps, two and three dimensional ERP map series, comparing ERP averages, channel ERP images, time-frequency plots, and ICA.

Event Related Potential (ERP) plots

The channel ERP image shows a decrease in the event-related potentials not only on the left side of the sensorimotor area but also on the right side during the movement, which indicates the desynchronization of the sensorimotor rhythms during the movement.

erps.png
Figure 6. Channels ERP image plot from -3000 to 1990 ms studied at the frequency range of 0 – 20 Hz with a significance bootstrap of 0.1 (10%.). The plots have been positioned according to the electrodes location.

ERP map series are known for containing ERP scalp maps at the specified latencies. In our thesis, we divided these latencies into two parts: before and after the movement onset. Thus, it is slighly noticed how ERP scalp maps changes among throughout -3000 and 1998 ms.

erp_msm
Figure 7. Power spectrum at the specified latencies of -3000, -2500, -2000, -1500, -1000, -800, -600, -400, -200, and 0 ms.

For one hand, ERP map series before the movement onset show evidence of contralateral activity at F3, Cz and P3 at the time of -2000ms, and at the time of -1000ms a slightly indication of contralateral activity at Cz and P4. Also, 400ms prior to the movement there is a hardly noticeable evidence of the same kind of activity at P4.

erp_msp.png
Figure 8. Power spectrum at the specified latencies of 0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, and 1998 ms.

Detection of MRCPs

For the detection of MRCPs we exported both ICA activity and EEG data from EEGLAB. First of all, we plotted applied a Gaussian Filter to the EEG Data in order to obtain a single trial.

events.png
Figure 9. Single Trial of EEG Data (blue) with Events plotted (yellow).

 

% Load EEG Data from EEGLAB
 load('Data.mat')
% Apply a Gaussian Filter
 virtualChannel =[-1/8 -1/8 -1/8 -1/8 1 -1/8 -1/8 -1/8 -1/8]*EEG(1:9,:);
 ans = virtualChannel;

This is how a random single trial MRCPs looks like:

 

singletrial.png
Figure 10. Random single trial of an MRCPs 1500ms before and 1000 after the movement onset of a finger-tapping experiment.

 

Afterward, we plotted the ERPs from each movement onset to visualize the shape of all single trials and plot the mean on the top.

mrcp.pngFigure 9. Average of all the epochs extracted with MATLAB from 1500ms prior to the movement onset to 1000ms after. The mean of all epochs is plotted in red.

% Plot mean MRCPs of all trials with all the trials. 
 figure ('name',['Mean MRCPs of all trials'],'NumberTitle','off') 
  for i=1:52 
   epochs(i,:)=ans(MovementOnset_index(i)-1500:MovementOnset_index(i)+1000); 
   plot(epochs(i,:)) 
   hold on 
   set(gca,'XLim',[0 2500]) 
   set(gca,'XTick',[0:500:2500]) 
   set(gca,'XTickLabel',[-1500:500:1000]) 
   xlabel('Time (ms)'); ylabel('Ampitude (\muV)'); 
   title('Mean MRCPs of all trials'); 
 end 
% Plot mean of all epochs 
 plot(mean(epochs),'r','LineWidth',2);

Moreover, if we only plot the mean of all epochs, we are able to identify all the MRCPs components that we introduced earlier in Figure 1. Scroll up the page and compare the figure below with Figure 1. You will notice that the shape of the MRCPs mean of all trials is similar. SUCCESS!

meanmrcp2
Figure 5. Mean MRCPs of all trials.
% Plot only the mean MRCPs line.
 figure ('name',['Mean MRCPs of all trials'],'NumberTitle','off')
 plot(mean(epochs),'r','LineWidth',2);
 hold on;
 set(gca,'XLim',[0 2500]);
 set(gca,'XTick',[0:500:2500]);
 set(gca,'XTickLabel',[-1500:500:1000]);
 xlabel('Time (ms)');
 ylabel('Ampitude (\muV)');
 title('Mean MRCPs of all trials');

For further information about the thesis, click here.

References

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