4.2.1
Experimental procedure
Fourteen healthy participants (7 males and 7 females, ages 26±4) participated in this EEG experiment. Ethical approval for EEG experimentation on humans was obtained from School of Systems Engineering, University of Reading, UK. Written consent was obtained from all
the participants. 12 participants were right handed and 2 were left handed. Participants did not have any previous experience of EEG experimentation.
This study was conducted to understand the neural correlates for detection of movement intention. A self-paced, asynchronous single finger tapping paradigm was developed to study EEG corresponding to motor command generation. Simple movement of index finger tapping was chosen as the task because it does not involve any complex hand gestures, directional reaching, grasping or trajectory planning.
EEG experimental paradigm was developed in Simulink using the tools provided by BioSig toolbox [205]. A customised tapping device was developed using a programmable micro-controller for recording the finger taps from both index fingers. One channel of binary finger tapping signal was recorded simultaneously with EEG for each hand. EEG and finger tapping signals were co-registered using tools provided by TOBI framework [208].
Participants were seated on a chair with palms placed on the table in front. Index fingers of both the hands were placed in finger caps of the tapping device. A fixation cross was displayed on the screen for 2 s at the beginning of each trial. It was followed by the instruction for right or left finger tap or resting state. A window of 10 s was given to perform the instructed task voluntarily at the time of the participant’s choice. Each trial was followed by a random break of 1 s to 1.5 s. 40 trials for each of the three conditions (right tap, left tap and rest)were recorded. 19 EEG channels in accordance with 10-20 international system were recorded using TruScan Deymed EEG amplifier. Ground and reference electrodes were placed at the centre and all the impedances were kept below 7 kΩ.
4.2.2
EEG Pre-processing, artefacts removal and segmentation
EEG was prepared for further analysis by performing pre-processing and artefacts removal on all EEG channels. DC offset was removed by subtracting the mean of each channel from the signal. All EEG filtering was done using fourth order Butterworth filter. A notch filter at 50 Hz was used to remove the power line noise. EEG was low-pass filtered with the cut-off frequency of 60 Hz to eliminate high frequency noise.
Eye blinks and some movement artefacts were removed using Independent Component Analysis [236]. Independent components with artefacts were identified manually and were eliminated. EEG was segmented into time locked trials of length 6 s by extracting 3 s prior to the onset of finger tap and 3 s after the onset of the finger tap. Channels F3, Fz, F4, C3, Cz, C4, P3, Pz and P4 over sensorimotor cortex were used for movement intention analysis.
4.2.3
Analysis of movement-related cortical potentials
MRCPs are obtained from lower frequencies by averaging several trials of EEG [53]. EEG was filtered between 0.1 Hz to 0.5 Hz to obtain movement related slow cortical potentials. Grand average MRCP was computed by averaging the all the trials from all the participants for nine channels. MRCP was also obtained for single trial.
4.2.4
Movement intention analysis based on exponential decay of auto-
correlation
Autocorrelation gives an estimation of how EEG is related to itself over time. Previous research shows that the autocorrelation changes during movement [204, 222]. Six virtual channels viz. F3-C3, Fz-Cz, F4-C4, C3-P3, Cz-Pz and C4-P4 were created using a longitudi- nal bipolar montage to enhance the movement related signal. EEG was band-pass filtered with cut-off frequencies 0.5 Hz and 30 Hz. Continuous autocorrelation was computed for each trial to determine the time development of the relaxation time of brain activity before, during and after the movement. Normalized autocorrelation was computed on 1 s window shifted by 100 ms from 6 s trial.
Let the single window of a signal be represented by A(t), the autocorrelation of A(t) is defined by C(∆t) = ⟨A(t)A(t − ∆t)⟩, where ⟨...⟩ represents the average over time. At zero lag, the signal is perfectly correlated, giving C(0) = ⟨A2⟩, and as lag approaches infinity, the signal becomes completely uncorrelated, giving C(∞) = ⟨A⟩2. The trend of relaxation process of autocorrelation could be described by C(t) = ⟨A2⟩e−tτ . Here, τ (decay constant) represents the relaxation time of autocorrelation.When autocorrelation is normalised, ⟨A2⟩ = 1.
To get the relaxation time of autocorrelation by capturing the exponentially decaying trend of autocorrelation, the exponential decay curve y = Ke−tτ was fitted to the local maxima points of positive lags of autocorrelation of each window in the trial (see Fig 4.1). The relaxation time τ was extracted as the feature at every 100 ms in the 6 s trial. The constant Kwas set to 1 as the autocorrelation was normalised. Changes in autocorrelation occurring during motor command generation were observed by studying the time progression of τ values.
4.2.5
Classification of movement and resting state trials
4.2.5.1 Classification of MRCP features.
MRCP for each 6 s trial was divided into 0.5 s windows by shifting it by 100 ms. Features for classification were obtained by averaging all the samples in each 0.5 s window. Feature from
0.2 0.4 0.6 0.8 1.0 (s)
Fig. 4.1 Exponential curve fitting representing decay of autocorrelation (autocorrelation relaxation) for right finger tapping trial in F3-C3.
first 0.5 s window (-3 s to -2.5 s) was used as resting state feature for training the classifier. Linear discriminant analysis (LDA) classifier was trained for every window in the trial with two class corresponding features from all the trials. 10x10 cross-fold validation scheme was used for this binary classification and percent classification accuracy was obtained. The threshold for classifier outcome was obtained from 95% confidence level (p < 0.05) for binary classification. The best performing channel was selected manually for each participant. 4.2.5.2 Classification of autocorrelation decay features.
Autocorrelation decay features (τ) were obtained for three classes viz. right finger tap, left finger tap and resting state. Classification was performed on each 1 s window shifted by 100 ms in 6 s trial. LDA classifier was trained for each window with tap features and resting state features from the corresponding windows in all the trials. 10x10 fold cross-fold validation scheme was used for binary classification and percent classification accuracies were obtained. The classification accuracies for 6 s trials for all the six virtual channels were plotted for all the participants (see Fig 4.2).