Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate

Figures & data

Figure 1. From EEG signal to microstates. (A) Original potential topography maps were extracted. (B) Original maps were analyzed by a cluster analysis to obtain four optimal classes of microstates. (C) Microstates sequences were obtained by fitting four classes microstates back to all data.

Table 1. Microstate parameters.

	Microstate class
	1		2		3		4
	Mean	SD	Mean	SD	Mean	SD	Mean	SD
MD (ms)
Left	36.87	3.149	52.33	6.813	45.37	1.221	42.65	2.094
Right	53.39	5.709	36.95	2.764	42.47	2.345	44.5	1.753
t-test P	0.995934		0.14749		0.000685		0.000912
TCR (%)
Left	9.157	4.843	31.53	6.788	32.25	3.396	27.44	3.562
Right	35.17	4.99	10.53	6.208	24.04	2.642	30.26	3.81
t-test P	0.719433		0.066471		0.000069		0.000168
OPS
Left	2.412	1.108	5.914	0.716	7.123	0.872	6.435	0.794
Right	6.586	0.588	2.764	1.453	5.683	0.763	6.821	1.003
t-test P	0.764336		0.085646		0.000005		0.000068

The mean and SD of the microstate parameters for two tasks as well as tests for group differents (unpaired t-test). Bold values indicated P < 0.05.

Table 2. Microstate classes transition probability[%].

	Left-hand		Right-hand
	Mean	SD	Mean	SD	t-test P
1→2	7.012	2.759	6.773	2.379	0.4971821
1→3	31.502	5.029	72.479	7.242	0.0000076
1→4	61.486	6.596	20.748	5.879	0.0000109
2→1	13.787	6.096	13.14	4.424	0.6090810
2→3	26.531	4.618	72.565	5.195	0.0000004
2→4	59.682	8.217	14.294	2.719	0.0000003
3→1	11.812	4.141	23.249	7.541	0.0008023
3→2	6.449	2.422	12.289	4.337	0.0002959
3→4	81.739	3.55	64.462	8.586	0.0002910
4→1	20.438	4.418	8.242	1.9	0.0000753
4→2	12.245	3.391	4.27	1.703	0.0005012
4→3	67.317	7.595	87.488	2.329	0.0000097

The mean and SD of the transition probability between different microstate classes for two tasks as well as tests for group differents (unpaired t-test). Bold values indicated P < 0.05.

Figure 2. Mean Duration of microstate class 3 and class 4 between left and right hand motor imagery. Gray line and triangle is the mean of each subject, dark line and diamond is the mean across all subjects.

Figure 3. Occurrence of microstate class 3 and class 4 between left and right hand motor imagery. Gray line and triangle is the mean of each subject, dark line and diamond is the mean across all subjects.

Figure 4. Time Coverage Ratio of microstate class 3 and class 4 between left and right hand motor imagery. Gray line and triangle is the mean of each subject, dark line and diamond is the mean across all subjects.

Figure 5. Transition probability from microstate class 1 to other states in left-hand motor imagery (L) compared to right-hand (R). Black column is the mean of each subject, red triangle is the average level across all subjects.

Figure 6. Transition probability from microstate class 2 to other states in left-hand motor imagery (L) compared to right-hand (R). Black column is the mean of each subject, red triangle is the average level across all subjects.

Figure 7. Transition probability from microstate class 3 to other states in left-hand imaging movent (L) compared to right-hand (R). Black column is the mean of each subject, red triangle is the average level across all subjects.

Figure 8. Transition probability from microstate class 4 to other states in left-hand motor imagery (L) compared to right-hand (R). Black column is the mean of each subject, red triangle is the average level across all subjects.

Table 3. Classification accuracy of different method and SD of 9 subjects.

		Acc(%) for subjects									Mean
Method	E	1	2	3	4	5	6	7	8	9	Acc	SD
TFDF	4	87.23	66.2	97.81	68.97	64.44	69.44	68.57	96.27	83.08	78	13.25
F score	4	89.36	69.01	97.81	66.38	66.67	72.22	68.57	97.01	90	79.67	13.55
FBCSP	22	94.44	52.77	93.05	65.97	88.19	60.41	70.13	94.44	93.05	79.17	16.73
SCSP	8.55	91.66	60.41	97.14	70.83	63.19	61.11	78.47	95.13	93.75	79.07	15.61
CSP	22	83.51	56.53	97.5	70.00	54.5	62.49	84.5	95.57	90.77	77.26	16.72
microstate	22	95.00	80.00	96.25	75.00	96.25	85.00	93.75	96.25	85.00	89.17	8.10

E represents the number of electrodes used in the classification.

Figure 9. ROC curves of the SVM classifier with the linear, polynomial and RBF kernel functions.