How to remove outliers in a matrix, according to two different column entries?

Hellow, I'm a bit novice in matlab coding. And i require your assistance.

I have a 3250x3 numeric matrix as depicted below and I want to identify and remove the latencies which falls outside the +-0.5 from the mean for each subject. Next, I want to average the latencies in the column 3 according to the trialcode (column2) for each subject (column1) and output as a matrix. Finally, I want to run a repeated measures ANOVA (2x2) according to the trial code.

I require assistance for the first two steps pimarily.

subject trialcode latency

8 4 340

8 4 328

8 3 218

8 4 338

8 3 213

8 4 328

8 3 254

8 4 323

8 4 340

8 3 273

9 3 580

9 4 363

9 4 371

9 3 374

9 3 383

9 3 302

9 4 406

9 3 390

9 3 380

9 3 366

9 4 468

I want to remove outliers for each subject across each trial code.

I tried the following codes which did not work :

[K, ~, G] = unique(Experiment1engS1(:, 1:2), 'rows')
mean= rmoutliers(K(:,3),'center','mean','ThresholdFactor', 2.5)

I also tried the for function:

Subject=[999];   
      % trialcode (1=mask_cong, 2=mask_incong, 3=nomask_cong, 4=nomask_incong)
   trialcode = [999];  
   % Latency
   latency = [999];



%calcolo delle medie

for i = 1:160:3250

    %Calcolo medie
    
   SUB_temp = mean(Experiment1engS1(i:i+159,1));
   trialcode_temp = mean(Experiment1engS1(i:i+159,2));
   latency_temp = rmoutlier(Experiment1engS1(i:i+159,3));
 
   
    %scrivo nelle matrici

    Subject=[Subject; SUB_temp]; 
    trialcode = [trialcode; trialcode_temp];
    latency = [latency; latency_temp];
    
end

This does not work, as some subjects don't have a total of 160 trials, as the data was pre processed to remove error trials.

I tried to use the splitapply, unique and rmoutlier, with no luck!

K= splitapply(@rmoutlier,Experiment1engS1(:,3),unique(Experiment1engS1(:, 1:2), 'rows'))

Kindly suggest what can be done. Thank you.